A crucial stage in medical image analysis for brain tumor diagnosis, treatment planning, and patient monitoring is brain tumor segmentation. It entails locating the tumor and any of its subregions, including the necrotic core, peritumoral edema and an enlarging tumor. Manual segmentation takes a lot of time and is prone to mistakes, which makes it unsuitable for regular clinical use. Recently, deep learning-based techniques have shown promise as a method for automatically segmenting brain tumors. In this work, we suggest a deep learning method for automatically segmenting brain tumors from magnetic resonance imaging (MRI) scans using a UNet architecture. A deep learning architecture created especially for image segmentation is the U-Net model. It is composed of an encoder-decoder structure, where the decoder reconstructs the input image and the encoder extracts features from it. On a range of medical image segmentation tasks, including brain tumor segmentation, the U-Net model has demonstrated state-of-the-art performance.