A crucial task in medical imaging is brain tumor segmentation, which allows for accurate diagnosis and treatment planning for patients with brain tumors. Magnetic Resonance Imaging (MRI) provides detailed volumetric data, but manual segmentation is time-consuming and prone to variability. Deep learning, particularly convolutional neural networks such as UNet3D, has emerged as a powerful tool for automating and enhancing segmentation accuracy. Accurate and efficient segmentation of brain tumors from multi-modal MRI scans remains challenging due to the heterogeneity of tumor appearances, varying MRI modalities (e.g., T1, FLAIR), and the need for robust models that generalize across diverse datasets. This study aims to develop and evaluate a UNet3D-based deep learning model for automated brain tumor segmentation, leveraging the BraTS2020 dataset to achieve high-precision delineation of tumor regions in MRI scans. We developed and trained a UNet3D-based model tailored for brain tumor segmentation, utilizing PyTorch and nibabel to process 3D MRI data from the BraTS2020 dataset. The model was comprehensively evaluated on standard datasets, demonstrating robust performance across multiple MRI modalities. We conducted a thorough comparison with baseline segmentation techniques, including traditional methods and other deep learning approaches, analyzing metrics such as Dice scores and segmentation accuracy. Our results highlight the model’s superior ability to delineate tumor boundaries, offering improved precision and efficiency over baselines, thus advancing the application of artificial intelligence in medical imaging for brain tumor diagnosis.