The primary objective of this project is to design and develop an AI-based system that monitors student engagement using computer vision and deep learning techniques. The system employs the YOLOv9 algorithm to detect facial expressions and behavioral patterns that indicate attention levels during online learning sessions. A web-based platform is created using HTML, CSS, and JavaScript to facilitate user interaction and display detection results. Flask serves as the backend framework, enabling seamless communication between the interface and the detection model. The system aims to provide accurate, real-time engagement detection and includes modular functionalities such as Home, Register, Login, Detection, and Logout for efficient operation. Additionally, the framework is designed to ensure scalability and allow for future enhancements in performance and functionality.