Traditional attendance monitoring methods, including manual roll calls, paper sign-ins, and RFID card systems, are usually time-consuming, error-prone, and inefficient in large-scale environments. These methods result in errors in attendance records, false attendances, administrative hassles, and confusion among students about recording their attendance. A more efficient, accurate, and reliable approach to managing and monitoring attendance is required, especially in schools and other organizations where accurate attendance records are critical. Our research provides a new approach to tracking attendance by integrating advanced software systems with advanced facial recognition technology optimized for higher accuracy. By utilizing computer vision techniques and machine learning algorithms, the proposed system attains a high accuracy rate of 97.99%, much higher than conventional methods and many similar systems. The technology processes photos, matches faces with a large database of 20 distinct images, and captures facial data using camera modules and a deep learning model. The system can detect multiple faces accurately at once in one frame and has anomaly detection to detect unregistered individuals, ensuring accuracy and security. It also prevents duplicate attendance entries and sends real-time email notifications to validate attendance, increasing transparency and trust. This automation significantly minimizes human labor, simplifies administrative processes, and enhances data accessibility. These improvements lead to scalability and flexibility, opening the door to wider applications in schools, corporate environments, and more.