Cerebral tumor segmentation is deemed to be highly critical challenge in therapeutic imaging diagnostics. Early detection of intracranial neoplasms means that it can be easily treated and the patient will most likely survive. The diagnosis of the cancer tumors is an exhaustive and time-intensive activity of isolation of the cerebral tumors in the massive set of magnetic resonance scans produced during the clinical process. We will need some sort of computerized automated system to separate the brain tumors and normal brain images. Automatic segmentation using deep learning offers state-of-the-art solutions compared to traditional methods. Advanced neural network approaches are useful for interpreting large-scale MRI datasets effectively and objectively. There is an abundance of literature describing the best practices in the extraction of neoplastic zones within magnetic resonance imaging-based cerebral images. Many recent studies report tumor classification accuracies above 95% and segmentation Dice scores exceeding 90%, especially using CNN-based hybrid and ensemble deep learning models. Unlike previous surveys, this paper uniquely categorizes deep learning approaches based on model architecture, hybrid strategies, and benchmark performance, offering a structured comparative evaluation that has not been presented before. The contributions include a focused review of CNN and transfer learning models, analysis of benchmark datasets such as BraTS and CE-MRI, evaluation of performance metrics from existing studies, and identification of research gaps for clinical deployment. In conclusion, deep learning approaches—especially CNN-based hybrid models—offer high accuracy and strong potential for reliable, automated brain tumor detection in clinical settings.