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Brain tumors are one of the most severe medical conditions affecting millions worldwide, requiring early detection and accurate diagnosis for effective treatment. This project focuses on the development of an automated brain tumor detection system using advanced image processing and machine learning techniques. The system is designed to analyze MRI (Magnetic Resonance Imaging) scans to identify the presence of tumors efficiently and accurately. The proposed methodology involves preprocessing MRI images to enhance quality, segmentation techniques to isolate regions of interest, and feature extraction methods to identify tumor characteristics. A deep learning model, specifically a Convolutional Neural Network (CNN), is employed to classify the MRI scans into tumor and non- tumor categories. The model is trained on a dataset of labeled MRI images, ensuring high precision and reliability in detecting abnormalities. This automated approach minimizes human intervention, reducing diagnostic errors and expediting the detection process. The system provides a cost-effective, non-invasive, and efficient solution that can assist radiologists and medical professionals in making accurate decisions. The project aims to enhance early diagnosis, ultimately improving patient outcomes and advancing medical imaging technology. Future enhancements may include integration with cloud- based platforms for real-time analysis and further refinement of classification accuracy through advanced deep learning techniques.