The early detection and continuous monitoring of brain tumors is critical for effective treatment and improved patient outcomes. This project presents AI Assisted Web Application for Brain Tumor Diagnosis named Tumor Track utilizing deep learning techniques to enhance diagnostic accuracy and streamline patient follow-up procedures. Our system leverages advanced neural networks to analyze medical imaging data, enabling precise identification and classification of brain tumors. Additionally, we have integrated a user-friendly patient follow-up system designed to facilitate seamless communication between healthcare providers and patients, ensuring timely updates and personalized care plans. The project not only improves diagnostic efficiency but also promotes proactive patient management, ultimately contributing to better healthcare outcomes. Preliminary results demonstrate the system's potential in achieving high accuracy in brain tumor detection and providing a robust framework for ongoing patient care. Future work will focus on further refining the deep learning models and expanding the system's capabilities to support a broader range of medical conditions.