Nowadays face recognition using applications of artificial intelligence is trending research problem. Attendance management is one of the applications where academician projects and business solutions are proposed. Staff and student attendance management is a critical requirement for schools and organizations to ensure compliance, accountability, and productivity. Through our analysis we found out that the conventional methods that are being used are manual processes such as roll calls, swipe cards that have RFID capabilities, pen and paper registers, although these methods are convenient and easy to use they lack real time monitoring, flexibility and safe storage of data and hence very unsafe for mass deployment. With the advances of the technologies used in Artificial Intelligence (AI), Computer Vision, and Machine Learning, AI-based attendance systems have been the natural choice. These attendance systems operate on facial recognition, biometric authentication, deep learning algorithms, and image processing methods to record attendance monitoring with higher precision and security. AI-based systems compare to the conventional ones insofar as the former do not involve human errors, minimize error rates, and offer instant access to information and analytics. This paper provides a brief overview of AI-based attendance management systems, describing best-in-class technologies such as FaceNet, OpenCV, Dlib, and Deep Face and their implementation methodology. We also describe a working example using Python, OpenCV, and face recognition libraries, structuring code snippets to demonstrate some functionalities. We also discuss technical challenges such as low-light support, occlusions, high computational overhead, and ethical challenges such as data privacy. Finally, we discuss future developments, including the integration of AI attendance systems with IoT, cloud technology, and blockchain for improved security, scalability, and remote access….