Student safety during daily school transportation remains a major concern, particularly in systems that rely mainly on GPS tracking and manual supervision. Existing approaches often lack proactive safety mechanisms for monitoring both student attendance and driver condition in real time. This paper presents MUTMA’INN derived from the Arabic word “مطمئن”, meaning being reassured, at peace, or tranquil, reflecting the system’s role in ensuring the safety and security of students during transportation. The proposed system is an AI-powered school bus safety framework designed to improve the security and reliability of daily student transportation in alignment with Saudi Vision 2030’s Quality of Life Program. The proposed system consists of two integrated components: a cross-platform Flutter mobile application for parents, drivers, and school administrators, and a Python-based edge system connected to Firebase for real-time synchronization. The framework automates student attendance through facial recognition at the bus gate, reducing manual effort and the risk of human error. In addition, it monitors the driver using contactless remote photoplethysmography and facial analysis techniques to estimate heart rate and detect signs of fatigue or emotional distress. When abnormal conditions are detected, immediate alerts are sent to administrators to support timely intervention. By combining mobile computing, edge intelligence, computer vision, and cloud services into a unified platform, MUTMA’INN provides a proactive approach to school transportation safety. The proposed framework demonstrates how AI can support safer and more intelligent student transit systems.