Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the foundational 3D U-Net architecture widely regarded as a benchmark for volumetric medical image segmentation to more advanced transformer-based models such as Swin UNET Transformers. Despite the success of 3D U-Net, challenges remain due to its reliance on local dependencies, which limit its ability to capture global context. Additional difficulties include accurately delineating complex tumor boundaries, addressing class imbalance across tumor subregions, and ensuring stable training of deep networks. This review comprehensively surveys the evolution of brain tumor segmentation techniques, emphasizing the transition from conventional U-Net models to cutting-edge Swin UNET transformer architectures. We discuss the impact of novel activation functions on improving gradient stability and segmentation accuracy. Furthermore, we explore complementary advancements, including weakly supervised learning, transformer-based frameworks for enhanced global context modeling, generative models for data augmentation, explainable AI for increased interpretability, and federated learning approaches for privacy-preserving collaboration. Key public datasets, such as BraTS, and standard evaluation metrics are reviewed to contextualize model performance. Lastly, we address ongoing challenges such as data heterogeneity, real-time clinical applicability, and integration barriers, and propose future directions for developing robust, interpretable, and scalable brain tumor segmentation systems. This review aims to provide researchers with a clear perspective on the next-generation evolution of deep learning methods that are shaping the future of brain tumor segmentation.
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