Detecting and classifying brain tumors is a critical and time-consuming task for medical professionals. To expedite this process and ensure accuracy, we explored the application of advanced technology, specifically deep learning models, for medical image segmentation. Our focus was on identifying a robust model for brain tumor segmentation using a public MRI imaging dataset consisting of 3064 TI-weighted images from 233 patients with meningioma, glioma, and pituitary tumors. In our study, we meticulously converted and pre-processed the dataset before delving into the methodology. We implemented and trained well-established image segmentation deep learning models, including U-Net, Attention U-Net with various backbones, Deep Residual U-Net, ResUnet++, and Recurrent Residual U-Net. We varied the parameters based on our comprehensive review of the literature on human brain tumor classification and segmentation. the applied approaches, the recurrent residual U-Net utilizing the Adam optimizer achieved a Mean Intersection Over Union of 0.8665. This model outperformed other state-of-the-art deep learning models in terms of accuracy. Visual findings showcased remarkable results in brain tumor segmentation from MRI scans, highlighting the algorithm's potential to automatically extract brain cancers and assist physicians in serving humanity more effectively. The efficiency of this approach offers promising implications for expediting diagnosis and treatment planning in the realm of neuro-oncology.