مجلة الاربعين المحكمة
The Arbaeen pilgrimage, one of the largest annual mass gatherings globally, presents significant challenges in maintaining public safety due to its immense scale, dynamic population movements, and vulnerability to physical and cyber threats. This study introduces an AI-driven framework designed to simulate, monitor, and analyze security risks associated with the pilgrimage, focusing on pedestrian and vehicular flow, drone surveillance, incident detection, and crowd sentiment. A synthetic dataset designed to approximate real-world conditions was generated using Python, integrating spatiotemporal features and environmental variables across key urban routes. Machine learning techniques—including Isolation Forest for anomaly detection and K-Means clustering for pattern recognition—were employed to uncover behavioural irregularities and high-risk crowd conditions. Additionally, visual analytics tools were used to map incidents, detect surveillance gaps, and identify relationships between crowd sentiment and security factors. The findings highlight the potential of advanced surveillance technologies, AI-enhanced analytical tools, and simulation modelling in informing early warning systems, optimizing emergency response, and enhancing situational awareness during the Arbaeen pilgrimage. This integrative approach offers a scalable and transferable solution for mass gathering security management in similarly complex contexts.