Due to the limitations of traditional roll-calling and biometric techniques for attendance recording, the system is difficult to adapt for large classrooms and wasting the time of teachers and students. We propose a YOLO based image driven automated attendance system for smart classrooms in this paper. In this paper, the YOLOv8 object detection algorithm is used for face detection in real-time, and the Video- based under the topic Face. By the use of a camera, the system takes classroom pictures, analyzing them with use of computer vision, and then records attendance automatically in a centralise database. The framework consists of six parts: image capturing, image preprocessing, face detection, face recognition, database management, and a web-based application to view attendance logs. The proposed system differs from traditional systems in that it increases efficiency, decreases attendance fraud, and eliminates the need for human interaction. The code is written in Python and makes use of OpenCV, YOLOv8, Face Recognition libraries, and MySQL database.  The deliverable includes increased accuracy, attendance tracking in real-time, lower paperwork burden, protected-data storage, and scalable implementation within educational institutions. This framework facilitates classroom automation and plays a role in the digital transformation of intelligent educational environment.