<p>Brain tumor segmentation from multimodal MRI scans is still a hard and crucial problem in medical imaging, and the outcome directly affects diagnosis, treatment plan, and patient prognosis. Current deep learning models like U-Net and its traditional variations tend to be limited in detecting fine-grained tumor boundaries and modeling multi-scale contextual information, especially when dealing with heterogeneous tumor structure and contrast-poor areas. These shortcomings result in poor segmentation performance, particularly in defining intricate tumor areas. To remedy these issues, we introduced MDS-ResUNet, an improved U-Net-inspired architecture that is particularly designed to enhance brain tumor segmentation precision. The model presented a few major innovations: (1) multi-scale residual learning, which enabled hierarchical feature aggregation across decoder levels to maintain semantic coherence and spatial resolution; (2) an atrous pyramid pooling (APP) module, enabling multi-scale contextual feature extraction by dilated convolutions, strengthening the model to detect tumors with different sizes and morphologies; (3) an edge-aware module (EAM), enabling tumor boundary defining by promoting edge feature representations explicitly; and (4) deep supervision, enforced on multiple decoder levels to encourage stable gradient flow and efficient multi-scale feature learning. Experimental verification was performed on the BraTS19 dataset, wherein MDS-ResUNet showed better performance with Dice values of 0.946 (Whole Tumor), 0.903 (Tumor Core), and 0.843 (Enhancing Tumor). Competitive IoU, sensitivity, and specificity measures were also reported. Through the proper resolution of the shortcomings of existing methods, MDS-ResUNet created a new state of the art in brain tumor segmentation, showing promising future applications in both deep learning research and clinical use.</p>