<![CDATA[The utilization of deep learning (DL) technology in brain MRI image analysis has seen significant advancements over the past five years. This study presents a systematic review of literature from 2020 to 2025, evaluating DL progress in automated tumor lesion segmentation, tumor type classification, genetic biomarker prediction, and treatment response monitoring. Various DL architectures, such as nnU-Net and ensemble models, dominate segmentation tasks, while transformer-based methods and foundation models are emerging as new pathways for large-scale medical image management. However, technical challenges including cross-institutional MRI protocol variations, underrepresentation of pediatric data, and model bias remain primary concerns. Initiatives like BraTS and federated learning approaches offer potential solutions to enhance DL model validity and scalability. This review highlights future directions for developing more adaptive, accurate, and ethical DL systems to support individualized and sustainable brain tumor diagnosis and management.]]>
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