A hybrid attendance management system has gained widespread popularity since its inception. Traditional methods such as the QR (Quick Response)-code-based approach and the Haar-cascade technique have been used widely for their cost-effectiveness. However, modern techniques such as RFID with IoT (Internet of Things) and Convolutional Neural Networks (CNN) are powerful tools that offer location-based tracking capabilities and improve accuracy. This study introduces a novel Hybrid Attendance Management System to overcome traditional attendance systems’ challenges, including pose invariance, illumination, and partial occlusion. It combines traditional and modern techniques to automate attendance tracking to improve accuracy and efficiency. This hybrid system captured attendance data by employing computer vision, a deep learning-based facial biometric recognition approach, and its integration with RFID technology. The results showed the accuracy of the attendance management system under different circumstances such as lightning, external objects, and inactivity of students. It also provided valuable insights into students’ attendance patterns, including the number of exits and entries, the percentage of inactivity during class, and time spent outside the classroom. These data facilitated categorizing students as present, late, or absent.