Atypical Teratoid Rhabdoid Tumor (ATRT) is an aggressive brain tumor in children, requiring precise imaging for accurate diagnosis and effective treatment planning. High-resolution Magnetic Resonance Imaging (MRI) is essential for visualizing ATRT, but traditional imaging methods face challenges in detecting and analyzing such rare tumors. This study examines the use of advanced machine learning models to improve MRI analysis for ATRT, aiming to enhance diagnostic accuracy and treatment outcomes. The study utilizes cutting-edge deep learning models, including Vision Transformers (ViTs), ResNet-based Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). These models were customized for specific tasks such as tumor detection, segmentation, and classification. To overcome the issue of limited data for rare tumors, techniques like transfer learning, multi-scale image processing, and synthetic data augmentation were applied. These deep learning approaches led to enhanced tumor segmentation, providing more detailed visual analysis, which is crucial for developing precise treatment strategies. By optimizing high-resolution MRI scans with these technologies, the study seeks to assist clinicians in making better-informed decisions for ATRT treatment. The integration of these advanced techniques shows promise for improving diagnostic precision and tailoring treatment plans, representing a notable advancement in pediatric neuro-oncology. Ongoing refinement of these methods is key to furthering progress in ATRT diagnosis and management.