The use of machine learning in the detection of brain tumors increases the outcome and result of diagnosing from medical images, particularly MRI. This study also points out the actualization of deep learning models like CNN with an ability to determine and categorize tumors accurately. Reduction in the reliance on ML-sourced interpretation increases cutting-edge tumor detection and differentiation and, thus, might result in benefits for patients. This study highlights the effectiveness of hybrid models, transfer learning, and preprocessing techniques in improving image quality and segmentation precision. Key challenges, including data scarcity and model interpretability, are discussed, as are future directions for refining models and expanding clinical application. Hence, the findings affirm that critical impediments like data availability and model interpretability do not inhibit ML from enhancing the discovery of brain tumors in principle, thus preparing the ground for implementing and deploying ML in actual clinical practice. This study suggests that deep learning can revolutionize brain tumor diagnosis, supporting early detection and optimized treatment planning.