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Manual attendance systems are widely used in educational institutions; however, they are time-consuming, error-prone, and vulnerable to proxy attendance. With increasing class sizes and the adoption of digital learning environments, there is a growing need for intelligent and automated attendance solutions. This paper presents a smart attendance System that automates face recognition and gaze-based attention monitoring to automatically mark attendance and assess student attentiveness in real world. “The system applies computer vision techniques with OpenCV for video handling, face recognition tools for identifying students, and Media Pipe Face Mesh for tracking gaze and estimating attention. Atten-dance data is recorded automatically with timestamps, and an automated Short Message Service (SMS) alert mechanism is implemented to notify parents of absent students. Testing shows that the system reaches about 94% recognition accuracy in regular classroom conditions, which helps reduce manual work and attendance fraud The system is cost-effective, scalable, and operates in real time using standard webcam hardware, making it suitable for development of modern educational institutions aiming to improve attendance management and classroom engagement.