Background/Objectives: Artificial intelligence is transforming neuroimaging by enhancing di-agnostic precision and treatment planning. However, its applications in pediatric cancer neu-roimaging remain limited. This review assesses the current state, potential applications, and challenges of AI in pediatric neuroimaging for cancer, emphasizing the unique needs of the pe-diatric population. Methods: A comprehensive literature review was conducted, focusing on artificial intelligence impact on pediatric neuroimaging through accelerated image acquisition, reduced radiation, and improved tumor detection. Key methods include convolutional neural networks for tumor segmentation, radiomics for tumor characterization, and several tools for functional imaging. We analyzed challenges such as limited pediatric datasets, developmental variability, ethical concerns, and the need for explainable models. Results: Artificial intelligence has shown significant potential to improve imaging quality, reduce scan times, and enhance diagnostic accuracy in pediatric neuroimaging. Artificial intelligence algorithms demonstrated improved accuracy in tumor segmentation and outcome prediction for tumor treatments. Conclusions: Artificial intelligence offers significant potential for enhancing pediatric neuroim-aging in cancer care, aiding in precise diagnoses and personalized treatments. To overcome current limitations, future research should focus on building robust pediatric datasets and developing interpretable models suited for clinical practice.