Brain tumor segmentation plays a crucial role in medical image analysis for diagnosis, treatment planning, and monitoring of brain tumor patients, and it is quite complex. To address this, we are introducing a new method for identifying brain tumors using powerful computer techniques called deep neural networks. Deep neural networks (DNNs) have demonstrated remarkable performance in various medical image identification and segmentation tasks. We employ a customized convolutional neural network architecture tailored specifically for the task of brain tumor identification and segmentation. The proposed network is designed to effectively capture spatial information and hierarchical features from the input images. Moreover, we integrate advanced techniques such as residual connections and attention mechanisms to enhance the network's performance. To train the network, we utilize a large dataset of annotated brain MRI scans to learn the complex patterns associated with different types and sizes of brain tumors. The proposed approach offers a reliable and efficient solution for precise delineation of brain tumors from MRI images, potentially aiding clinicians in treatment planning and patient care.