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Brain tumor remain one of the most life-threatening forms of cancer, and early and accurate diagnosis is crucialfor effective treatment planning and improving patient outcomes. Magnetic Resonance Imaging (MRI) serves as a primary modality for brain tumor detection; however, manual interpretation of these scans is often time-consuming and subject to inter-observer variability. Recent advances in Machine Learning (ML) and Deep Learning (DL) offer promising tools to automate and enhance tumor detection and segmentation in medical images. This research paper presents a comprehensive study on the application of ML and DL techniques for brain tumor detection, focusing on both classification and segmentation tasks. Various algorithms, including traditional ML classifiers and state-of- the-art Convolutional Neural Networks (CNNs), were evaluated on publicly available datasets. The proposed deep learning models demonstrated superior performance in identifying tumor regions with high accuracy and robustness. Furthermore, the paper discusses the challenges associated with data preprocessing, model interpretability, and real- time deployment, particularly in the context of medical science. The results underscore the potential of integrating ML/DL-based systems into clinical workflows to support radiologists and enhance diagnostic efficiency.