An automated Attendance System utilizing Face Recognition provides an advanced, efficient solution for managing student attendance by using machine learning algorithms to accurately identify individuals based on their facial features and automatically log their attendance. The system incorporates image processing techniques such as face detection and feature extraction, utilizing libraries like OpenCV, NumPy, Pandas, and Insight face for effective data handling. It identifies faces in images, extracts distinct facial characteristics, and employs machine learning models to improve recognition accuracy. Designed to be scalable and user-friendly, the system integrates with Redis for fast and efficient data storage, with a registration form for easy addition of new individuals. A real-time prediction interface via Stream lit enables seamless attendance tracking with live face recognition, automatically recording attendance and generating detailed reports for analysis. This system streamlines attendance management, reduces human error, and eliminates the risk of proxy attendance, offering a comprehensive, automated solution for real-time attendance monitoring.