1. Introduction

2. Background on Smart City and the Metaverse

3. AI-enabled Metaverse for Smart City

4. Sustainable Metaverse for Smart City

4.1. Applications and Use Cases

Smart cities can be defined by the following domains: smart environment, smart mobility, smart energy, smart health, smart governance and smart economy. We consider these domains in our discussion on the applications and use cases for sustainable smart cities, and we summarise some applications in Table 4.

4.1.1. Smart Environment

A smart environment encompasses a range of services to manage pollution-related issues, including air quality, waste management, water pollution, radiation pollution, agricultural systems, and ocean environment monitoring [22,175,176]. It aims to enhance the quality of life while minimising environmental footprints through technology, contributing to sustainable urban living. The Metaverse plays a transformative role in creating smart environments for smart cities by using virtual simulations to assess the consequences of urban projects, such as new constructions or changes in land use, thereby promoting sustainable growth and reducing pollution. The Metaverse will benefit asset-heavy sectors such as utilities and power systems by leveraging AI integrated with other key technologies such as IoT, DT and XR. For instance, simulation of various environmental conditions, such as changing weather patterns, day-night cycles, or natural disasters [177,178,179], is made possible. Several studies have explored the integration of advanced AI techniques in environmental applications within smart cities, particularly in the areas of air quality control [180,181,182], pollution classification [183], waste management [184,185] , building management [151,186] and remote sensing [187] for monitoring and managing urban environments.

Table 4. A summary of AI-enabled applications and use cases in the Metaverses across various domains in smart cities.

Table 4. A summary of AI-enabled applications and use cases in the Metaverses across various domains in smart cities.

Domain	Sub Domain	Reference
Smart Environment	Air quality control	[32,180,181,182]
	Environmental monitoring	[183,187]
	Disaster planning	[177,178,179]
	Water management	[188]
	Agriculture	[189,190,191,192,193,194,195]
	Waste Management	[176,184,185,196]
	Infrastructure management	[151,186]
Smart Mobility	Traffic flow prediction	[90,92,163,197,198]
	Traffic monitoring	[199,200]
	Traffic condition analysis	[201,202,203,204]
	Autonomous vehicles & predictive maintenance	[121,205,206,207,208,209,210]
Smart Energy	Energy management & forecasting	[211,212]
	Energy optimisation	[186,213,214]
	Power grid management and monitoring	[215,216,217,218]
Smart Health	Pandemic forecasting	[219,220]
	Public health	[221]
	Medical care & management	[222,223,224,225,226]
	Medical training	[227,228]
Smart Governance	Electronic voting	[229]
	Decision-making and service delivery	[230,231,232,233]
Smart Economy	Smart payment systems, e-business	[234]
	Smart manufacturing	[235,236,237]
	Enhance customer experience	[238]

Central to this effort is the deployment of IoT-based sensors, which, despite their cost-effectiveness, face challenges related to large-scale installation and maintenance. To address these challenges, research has focused on hybrid IoT frameworks and intelligent system designs that ensure feasibility and cost-effectiveness in monitoring environmental factors such as pollution, waste, and climate-related variables [22]. For example, the application of AI and ML in these systems enables the intelligent analysis and prediction of environmental changes, contributing to better management of air quality, waste, water pollution, and climate change issues [189]. In addition, AI-driven solutions are crucial in addressing environmental inequalities in urban areas, where real-time data from air quality monitoring stations can inform policies to improve conditions in vulnerable communities [41]. AI contributes to water management by enabling efficient monitoring and prediction of water demand and reducing waste through smart network systems [188] and facilitates precision farming, disease detection, and resource management in agriculture [190,191,192,193,194,195], leading to more sustainable practices [239]. In waste management, AI can enhance the effectiveness of handling and sorting recyclables. For example, Troulaki et al. [196] introduced the RECLAIM4 project, which integrates AI, data and a robot-based portable material recovery facility, enabling the decentralised treatment of recyclable waste in cities.

4.1.2. Smart Mobility

Smart mobility relies on technology to develop transportation and logistics networks. It encompasses a variety of transportation services, including gas and electric vehicles, traditional vehicles, taxi tracking and sharing services (e.g., Didi, Uber, Lyft), bike/scooter/bicycle tracking, rail lines augmented and educating systems related to mobility [22]. These services aim to enhance citizens’ mobility by reducing congestion, improving safety, and benefiting the economy. AI enables smart mobility in the Metaverse by predicting traffic flow [197,198], monitoring traffic [199,200], analysing traffic conditions [201,202,203], managing congestion [46] and developing autonomous vehicles [207,208,209]. Moreover, combined with DT, it simulates real-world traffic conditions, improving public transportation routes and assisting urban planners, traffic engineers, and policymakers in making better-informed decisions about urban planning [86]. For instance, in recent years, the automotive industry has seen significant advancements, particularly in autonomous vehicles, driven by technologies like IoT and AI. These technologies have enhanced vehicle features, ensuring safety and more precise operation with less human involvement. Despite these improvements, challenges remain in equipping vehicles with such capabilities, and testing in both simulated and physical worlds poses difficulties. The Metaverse offers a potential solution by providing a virtual testing environment where vehicles can be trained on extensive data from autonomous vehicles worldwide, closely replicating real-world conditions [240]. Furthermore, AI models equip vehicles to detect and classify various environmental elements by recognising stationary objects, road signs, barriers, and moving entities, such as pedestrians and other vehicles. This is crucial for safe and informed navigation, providing the comprehensive situational awareness necessary for effective decision-making by the vehicle [88]. For example, Tizedes et al. [204] developed a system utilising AI to detect road hazards like potholes and speed bumps by combining vision-based object detection with LiDAR technology for precise and efficient hazard identification.

Furthermore, predictive analytics, which relies on AI models, machine learning algorithms and data mining, is crucial in urban mobility management in the Metaverse. It achieves this by integrating historical traffic patterns, real-time vehicle flows, weather conditions, and social media trends to accurately predict traffic behaviours and congestion points and recommend efficient routes, crucial for optimising traffic flow, reducing travel times, and aiding emergency response. Additionally, AI models adapt to environmental changes and real-time events, offering a comprehensive approach to managing urban mobility through predictive maintenance [210]. For instance, Nuvvula et al. [205] introduced an innovative strategy for optimising electric vehicle (EV) fleet operations by integrating predictive analytics with renewable energy sources, utilising real-world data and advanced machine learning techniques. The approach improved predictive maintenance, charging schedule optimisation, and route planning, resulting in a reduction in greenhouse gas emissions and cost savings. Bellini et al. [206] presented a model that collects and manages diverse mobility data, including traffic patterns and sensor information, to predict and simulate the impact of different scenarios on urban transportation. The model was visualised using a 3D City Digital Twin on the Snap4City5 platform, providing decision-makers with realistic and immediate insights for more effective urban planning.

4.1.3. Smart Energy

Smart energy involves using technologies such as AI, IoT, XR, smart grids, and renewable energy sources to create an energy system that monitors, predicts, and adapts to energy demand and supply changes. In the Metaverse, smart energy systems will enable the support of the seamless integration of renewable energy sources, intelligent storage solutions, and decentralised microgrids, which are crucial for maintaining energy stability, reliability and sustainability in urban infrastructure. The Metaverse allows for visualising, simulating, and optimising energy systems, enhancing decision-making processes and enabling proactive responses to energy demands. AI enhances the efficiency and reliability of renewable energy (RE) by enabling accurate forecasting of RE supply, energy and grid management [212,217,218], through centralised control systems [241] and analysing power systems through forecasting [211]. Integrated with DT, AI can model interactions between energy sources, distribution networks, and consumers to optimise energy consumption in buildings [214] and across the city by implementing energy-saving measures and integrating renewable energy sources [186].

Maintaining smart grids, especially when microgrids are spread across various geographical locations in a smart city, is challenging. AI, in combination with XR and IoT, can enhance the efficiency of maintaining geographically dispersed generation assets by providing cost-effective remote assistance and virtual site inspections, thereby improving workforce productivity and operational efficiency through advanced data analytics and real-time monitoring [44]. For instance, an IoT-based framework for predicting power generation from renewable energy sources in smart microgrids, crucial for energy management in smart cities, was presented [215]. This framework integrates data from wind and solar sources. It employs a hybrid model combining a multi-head attention-based deep autoencoder with an extreme gradient boosting algorithm for accurate forecasting. Similarly, He et al. [216] introduced a data-driven situation awareness (SA) paradigm, DT-SA, utilising a digital twin model for the Energy Internet of Things (EIoT-DT) to address challenges in modern energy systems by combining machine learning and data technologies, improving system performance through continuous virtual-real interaction and feedback. Case studies were also presented, such as smart city EIoT-DT construction of Lingang, China and power grid monitoring—additionally, Stavropoulos et al. [213] explored how integrating IoT and smart grid technologies in residential settings could significantly enhance energy efficiency and management, balancing energy demand and supply to create sustainable urban environments.

4.1.4. Smart Health

Smart health leverages wearable devices, IoT, and wireless communication technologies to navigate health information, connect individuals and resources, and efficiently manage and respond to health environment demands intelligently [242]. In the Metaverse for smart city, AI, wearables, and the Internet of Nano-Things (IoNT) can help track infectious diseases by leveraging real-time medical data from large populations [219,220]. These technologies enable the early detection of outbreaks and allow healthcare providers to take swift action to prevent the spread of diseases [243]. For instance, Farooq et al. [219] introduced a DNN-based, data stream-driven real-time online incremental learning algorithm to analyse the transmission dynamics and prevention strategies for COVID-19, offering insights for pandemic forecasting and policy recommendations. Haptic technologies can also allow physicians to remotely interact with patients, which is particularly beneficial for high-risk groups such as healthcare workers during pandemics. Additionally, 6G technology significantly enhances emergency response efforts by improving the accuracy of positioning systems, allowing rescue teams to locate individuals whose lives might be at stake more effectively during natural disasters [221,244] like wildfires, earthquakes [243] and accidents. Integrating AI and holographic technology in ambulance services further exemplifies the potential of smart health in the Metaverse. These will enable hospitals and ambulances to be connected, where doctors can conduct rapid assessments of patients in ambulances with the support of paramedics.

Furthermore, the Metaverse enables a shift from 2D to immersive 3D virtual care, offering advanced applications such as surgery assistance, health management, and real-time monitoring of patients in a more interactive environment [44]. AI plays a crucial role in these advancements by enhancing the capabilities of Internet of Medical Things (IoMT) devices, improving the accuracy of diagnostics, and enabling continuous management and monitoring of health conditions such as heart rate and blood glucose levels [46] and chronic illnesses [223,224,225]. Additionally, AI-enabled healthcare simulations within the Metaverse provide a safe and controlled environment for medical professionals to practice and refine their skills through realistic, dynamic scenarios [227,228] and aid in training patients with disabilities [222]. Moreover, AI-powered virtual assistants like chatbots within the Metaverse can help users navigate healthcare services, answer health-related questions [226], and perform tasks, enhancing the user experience. The Metaverse can also enable AR/VR therapy sessions in calming or therapeutic environments, helping patients manage stress, anxiety, or other mental health conditions [86].

4.1.5. Smart Governance

Smart governance leverages technologies to enhance decision-making, policy development, and service delivery. It also involves the innovative application of ICT to facilitate information exchange, communication, and collaboration with stakeholders through various forms such as social media, blogs, public discussion forums, word of mouth and surveys to enhance the delivery of government services. Based on various studies, a generic smart governance framework involves collecting data from stakeholders like citizens, applying ICT technologies like AI to extract insights, and integrating public feedback into policy updates [22]. In smart governance, AI-based methods optimise decision-making, enhance service delivery, and increase citizen participation. Studies have shown that AI’s integration with social innovation (SI) can significantly enhance smart decision-making (SDM) in governance, as evidenced in South Korea and Pakistan [230]. Abu Qasem et al. [231], in their research project, Ageing Smart6 – Smart structured areas, demonstrated how Decision Support Systems (DSS) play a crucial role in promoting inclusiveness in smart city design by ensuring that services are accessible to all citizens, particularly those who are vulnerable. Khansari et al. [232], developed a prototype of DSS based on a multi-agent system and applied it in areas such as census data, energy reporting, and social science theories, demonstrating its potential in urban planning.

Furthermore, with IoT and blockchain, AI enhances government operations’ security, transparency, and traceability, including voting systems and identity management. For instance, decentralised blockchain technologies like NFTs in electronic voting enhance transparency and security in governance processes, addressing challenges like voting fraud [245]. NFTs provide unique identities for participants, ensuring that only verified individuals can vote, thereby promoting transparency and trust in the decision-making process. As an example, Singh et al. [229], proposed a privacy-preserving and anonymous e-voting system with Proof of Vote, ensuring secure and private voting on public blockchains. Similarly, Decentralized Science (DeSci), leveraging Web3 and Decentralized Autonomous Organization (DAO) technologies, exemplifies an application of smart governance by fostering transparent, inclusive, and efficient management of scientific processes, aligning with the core principles of decentralisation and adaptive decision-making [233]. This approach can be adapted to smart city government services in the Metaverse.

4.1.6. Smart Economy

The concept of a smart economy involves the integration of advanced technologies such as AI, blockchain, and big data analytics in areas like e-business and e-commerce, including manufacturing and service delivery, to create innovative and sustainable economic models. In the Metaverse for a smart city, these technologies optimise business processes, enhance financial transactions through smart payment systems and blockchain, and drive new industries focused on sustainability, such as green energy and circular and sharing economy [46]. For instance, Abraham et al. [234] developed a smart toll transaction application with an AI-enabled multi-agent system and blockchain for dynamic pricing and traffic optimisation for smart transportation systems in smart cities. The Metaverse in a smart city offers unique marketing opportunities where brands can interact with users in immersive virtual environments, which integrates real-time advertising within gaming [36] and social media platforms. In addition, one assumption about the Metaverse is the possibility of a creator economy, where trading assets across different spaces is facilitated [246]. Additionally, blockchain technology is pivotal in the Metaverse by fostering new avenues for digital commerce and interaction [36] and overcoming trust and security challenges in peer-to-peer marketplaces [245].

AI further enhances these applications by providing personalised recommendations, virtual assistants and chatbots that mimic human interactions, creating a more engaging and tailored user experience [247]. Additionally, in a smart economy, the Metaverse supports entrepreneurship by establishing new business models, which reduce physical store costs, streamline supply chains, and enhance customer interaction through immersive experiences [248]. For instance, Kim et al. [238] explored the differences in consumer perceptions and behaviours when engaging with AR and VR technologies in virtual shopping environments, aiming to understand how these immersive technologies affect consumers’ experiences, attitudes, and behavioural intentions. The consumers were provided with a realistic and enjoyable shopping experience by simulating the touch of fabric and the smell of perfume. In smart manufacturing, Sun et al. [236,237] introduced a learning-based framework designed to help acquire complex 3C assembly skills using a multimodal digital-twin environment, and Hou et al. [236] developed a hybrid residual multi-expert reinforcement learning method for interactive learning in the digital industrial Metaverse. Zhang et al. [235] proposed a multicategory aggregated monitoring framework to enhance production performance monitoring under diverse working conditions, improving visualisation and interaction, helping analysts detect and understand abnormal performance in heavy-plate production data, particularly in complex operations with long steps and high precision.

6. Analysis of the Findings

1)

Integrating AI with key technologies such as XR, digital twins, blockchain, 5G/6G, and IoT is essential for building sustainable smart cities within the Metaverse. AI optimises resource management, enhances energy efficiency, and supports real-time decision-making across various domains, including agriculture, transportation, energy and health. XR and digital twins simulate environmental conditions and urban scenarios, enabling cities to reduce waste, improve infrastructure planning, and mitigate environmental impacts. Blockchain ensures secure data management, while edge AI and 5G/6G enhance the seamless transmission of massive amounts of data between the virtual and physical worlds, enabling real-time responses critical for sustainable city operations. These technologies foster a sustainable, energy-efficient, and data-driven urban ecosystem.

2)

Several use cases illustrate the role of AI and Metaverse technologies in advancing sustainable smart cities. For example, AI and IoT technologies in smart environments manage air quality, waste, and water pollution by leveraging real-time data for environmental monitoring and decision-making. XR and digital twins simulate environmental conditions and urban scenarios, enabling cities to reduce waste, improve infrastructure planning, and mitigate environmental impacts. In smart mobility, AI-driven predictive analytics optimise traffic flow and reduce congestion, contributing to lower emissions. Smart energy systems, supported by AI, digital twins, and IoT, allow efficient energy management and integration of renewable energy sources. Early detection of diseases and optimised emergency responses reduce strain on healthcare resources, contributing to social sustainability by improving public health outcomes. Similarly, AI-enabled decision support systems in smart governance help policymakers develop sustainable urban policies, while in the smart economy, AI and blockchain enable innovative business models, enhancing economic sustainability. A few case studies highlight the impact of AI and the Metaverse in creating sustainable smart cities. For instance, the RECLAIM Project initiative applies AI and robotics to decentralise waste management, enabling smart cities to address sustainability challenges effectively.

3)

Integrating AI-enabled Metaverse technologies in smart cities presents significant benefits, highlighted above. However, challenges such as data privacy, cybersecurity, and the need for scalable infrastructure remain critical. Ensuring the interoperability of diverse systems and addressing the ethical implications of AI decisions are essential for achieving sustainability in the Metaverse for smart cities. Future research should focus on developing interoperable AI systems, explainable AI models, and enhanced privacy-preserving techniques to ensure that AI-driven smart city applications operate responsibly and equitably within the Metaverse. Further, federated learning, 6G networks, collaboration among stakeholders, and developing secure governance frameworks are essential for long-term sustainable growth.

4)

While this study provides in-depth insights into current AI techniques and AI-enabled technologies and their applications within the Metaverse for smart cities, it has certain limitations. The rapid pace of technological advancement means that some of the technologies discussed may evolve, necessitating continuous updates to the findings presented. The study is also limited by its reliance on existing literature and technologies available at the time of writing. Furthermore, the paper primarily focuses on technological aspects, with less emphasis on socio-economic factors and user adoption challenges, which are critical for the successful implementation of AI in real-world scenarios essential for the Metaverse in smart cities. Recognising these limitations, future research should adopt a more holistic approach, incorporating technological advancements and addressing the human and societal dimensions of technology integration.

6. Future Directions

7. Conclusion

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