Brain tumors currently represent the most dangerous disorders affecting human bodies today. Tumors appear throughout different regions within the brain. Medical staff find it hard to spot tumors in various body regions followed by rapid treatment to cure the tumors. The occurrence of brain tumors shows growing statistics based on newest scientific findings. A brain illness manifests through symptoms including problems with hearing and speech together with regular headaches and memory problems and changes in hearing and personality. The process of manual brain MRI segmentation has become an increasingly challenging problem for modern researchers. A complete classification system determines accurate identification of brain tumors together with their brain placements. Medical image segmentation together with volume estimation serves as essential medical instruments for both radiation therapy and medical practices. Tumor position inside the brain assists medical staff in determining which factors contribute to normal body function. Medical professionals use accurate brain tumor segmentation from MRI data as a key requirement for conducting various clinical examinations including diagnosis as well as treatment planning and patient tracking. This review paper evaluates recent AI developments for brain tumors through machine learning and deep learning models with Convolutional Neural Networks which boost medical image analysis and minimize healthcare delays. Our main objective centers on building an automated system which provides accurate results while operating with high efficiency to support brain tumor early diagnosis and treatment processes. This paper examines the detailed investigation into project background significance as well as the research approach.