The purpose of this project is to revolutionize the way colleges manage student attendance by providing a cutting-edge automated system that operates in the cloud and utilizes deep learning and big data. The system automates critical operations such as photo capturing, real-time face detection, and face recognition by coordinating processes with Apache Airflow. The first step is to capture a photograph of a student using the camera. Instant facial identification by the MTCNN algorithm identifies the student. FaceNet analyzes detected faces and creates high-dimensional embeddings. These embeddings are linked to each student’s database label and ID. Automatic attendance tracking via student identification is achieved by comparing embeddings to known faces at predefined intervals. This system creates a trustworthy automated student attendance management system to make monitoring more visible, efficient, and accurate. The solution that has been recommended has its objective as the creation of an automated student attendance management system that offers improvements in monitoring precision, effectiveness, and transparency. From the system’s design to its integration with big data technologies and workflow orchestration, this website covers all there is to know about the system in great depth. Experimental findings suggest the system can identify enroll students. This article examines the novel notion of employing cutting-edge technology to track attendance, which might revolutionize the system. A full attendance management solution, this complicated system advances computer vision, deep learning, and educational technologies.