Traditional attendance tracking systems in educational institutions remain heavily reliant on manual methods such as paper registers or digital sign-in sheets, which are plagued by three critical limitations: susceptibility to proxy attendance, significant administrative time consumption, and vulnerability to record tampering. This study addresses these challenges by developing an AI-powered attendance system using facial recognition technology. Implemented with Python and the Local Binary Patterns Histograms (LBPH) algorithm, the solution captures over 100 facial images per student during enrollment to achieve 95% recognition accuracy. System testing at Umm Al-Qura School demonstrated dramatic improvements: reducing processing time to 1–2 seconds per student (95% faster than manual methods) while maintaining robust performance across lighting variations and diverse student appearances (including hijabs and hairstyle changes). The modular architecture combines OpenCV for real-time processing with secure local data storage, eliminating cloud dependency and enhancing privacy. These results validate facial recognition as an effective solution to the accuracy, efficiency, and security limitations of conventional attendance systems.