AutoAttend is an AI-driven attendance and attention monitoring system designed to automate and enhance classroom and workplace management through facial recognition technology. The system utilizes computer vision and deep learning algorithms to detect, encode, and recognize human faces in real time, eliminating the need for manual or touch-based attendance procedures. Built using Python, OpenCV, and dlib’s ResNet-based face encoding, the application accurately identifies individuals from live camera feeds, records their attendance in a secure SQLite database, and simultaneously evaluates attention levels through visual cues such as eye aspect ratio (EAR), mouth aspect ratio (MAR), and gaze direction. The primary objective of AutoAttend is to establish a reliable, contactless, and intelligent attendance system that minimizes human intervention and prevents proxy attendance. By incorporating attention analysis, the system extends beyond simple presence detection to provide real-time insight into user engagement. Its modular architecture supports scalability for diverse environments such as educational institutions, corporate offices, and online learning platforms. The project demonstrates the effective integration of facial recognition and behavioral analytics to enhance automation, improve operational accuracy, and promote interactive learning and work force management.