Efficient toll processing is essential for reducing traffic congestion and enhancing transportation network operations at toll stations. This study examines the Neelamangala Toll Plaza on India's National Highway 48, focusing on the potential of artificial intelligence (AI) to optimize toll processing. A detailed work following with case study of the Neelamangala Toll Plaza was conducted, with machine learning algorithms utilized to analyze data and predict traffic patterns as vehicles approached the toll station. The system integrated AI models—specifically, a Supervised Learning (SL) time series model for traffic prediction and Reinforcement Learning (RL) based on a Markov Decision Process (MDP)—alongside a randomized algorithm to dynamically adjust to real-time traffic conditions. The randomized algorithm facilitated equitable task distribution, preventing system overload during peak hours. System performance was assessed using key metrics: Average Processing Time (APT), Queue Length Reduction (QLR), and Throughput (TP), which measured the system’s ability to manage high traffic volumes and mitigate congestion. The AI-powered model demonstrated significant improvements in processing times, queue length reduction, and overall vehicle flow, outperforming traditional methods in both speed and scalability. AI-driven toll management techniques reduce processing times by approximately 35%, decrease queue lengths by 28%, and increase throughput by 40% compared to traditional toll processing systems. These findings suggest a robust, adaptive solution for modern toll systems, with broader implications for efficient and sustainable transportation infrastructure