Brain tumors are classified as either benign or malignant. Benign tumors maintain a uniform structure and do not contain cancer cells, while malignant tumors grow rapidly, exhibit irregularities, and contain a large number of active cancer cells. Benign types such as gliomas and meningiomas typically grow at a slower rate by imitating living cells. Image segmentation and classification are crucial in brain tumor analysis, supporting tissue differentiation, tumor localization, and size assessment. Accurate segmentation is essential for proper diagnosis and treatment. This study introduces a 3D U-Net model for brain tumor segmentation using the BraTS 2023 MRI dataset. The 3D U-Net architecture is highly effective due to its ability to leverage spatial information from 3D data by proving its capability in accurately segmenting brain tumors for clinical applications.