Brain tumor segmentation is one of the most challenging jobs in medical image analysis. The goal of this process is to accurately delineate the regions affected by brain tumors. In recent years, deep learning techniques have producedencouraging outcomesin various computer vision challenges, like image classification, object detection, and semantic segmentation. These techniques have also been effectively applied to brain tumor segmentation, yielding encouraging outcomes. This review provides a comprehensive analysis of the latest deep learning-based approaches for brain tumor segmentation, highlighting the significant advancements made with cutting-edge technologies. Initially, a brief outine of brain tumors and the methods used for their segmentation is presented.Next, cutting-edge algorithms are covered, with an emphasis on the most current evolutions in DL techniques. Ultimately, a review of the situation is given, along with an outlook on how MRI-based brain tumor segmentation techniques will progress going forward addressing in great detail technological topics including multi-modality processes, segmentation under imbalanced situations, and network architecture design. We also offer enlightening talks about potential future paths for development.