This thesis presents the development of an automated student attendance management system using facial recognition technology and Raspberry Pi. The project takes issues found in manual attendance methods such as human error, time wastage and administrative burden. By integrating Python, Flask, and OpenCV, the system achieves accurate face detection and recognition, while Power Automate enhances functionality through automated messaging features. The system was tested successfully, showing improved accuracy and reduced processing time compared to manual methods. Although limitations such as poor lighting and hardware constraints were noted, the project provides a solid foundation for future improvements in low-cost, intelligent attendance systems. Overall, the work shows strong technical understanding and contributes meaningfully to the advancement of automated attendance systems.