Automated brain tumor segmentation from magnetic resonance (MR) images plays a crucial role in precise diagnosis and treatment monitoring in brain tumor care. Leveraging the Brain Tumor Segmentation Challenge (BraTS) dataset, this paper introduces an extended version of the nnU-Net architecture for brain tumor segmentation, addressing both adult (Glioma) and pediatric tumors. Our methodology integrates innovative approaches to enhance segmentation accuracy. We incorporate residual blocks to capture complex spatial features, attention gates to emphasize informative regions and implement the Hausdorff distance (HD) loss for boundary-based segmentation refinement. The effectiveness of each enhancement is systematically evaluated through an ablation study using different configurations on the BraTS dataset. Results from our study highlight the significance of combining residual blocks, attention gates, and HD loss, achieving the best performance with a mean Dice and HD score of 83%, 3.8 and 71%, and 8.7 for Glioma and Pediatrics datasets, respectively. This advanced nnU-Net showcases the promising potential for accurate and robust brain tumor segmentation, offering valuable insights for enhanced clinical decision-making in pediatric brain tumor care.