In the realm of online learning, ensuring accurate student attendance tracking is essential for effective education management. Conventional methods like manual sign-ins or digital codes are susceptible to inaccuracies and can be manipulated, compromising the integrity of attendance records. To address this challenge, we propose the incorporation of iris recognition technology into online learning platforms. Iris recognition provides a robust, non-intrusive means of authentication based on the distinct patterns of individuals’ irises. This integration facilitates seamless and secure identification of students during online learning sessions, enhancing the reliability of attendance records. Upon enrollment, students’ iris images are securely captured and associated with their profiles within the learning management system. Instructors can then utilize iris recognition scanners to swiftly verify student identities and record attendance data accurately. By leveraging machine learning algorithms, we ensure the precision and efficiency of iris recognition in student attendance tracking. This innovative approach not only streamlines the identification process but also minimizes errors, thereby improving overall administrative efficiency in online education settings. Ultimately, the integration of iris recognition technology promises to elevate the quality of education management by fostering greater accountability and reliability in student attendance tracking during online learning sessions.