AI-Driven Risk Management Framework for Hajj Seasons: Predictive Models for Proactive Safety and Resilience

Annual Hajj pilgrimage is one of the most complicated mass events in the world with millions of pilgrims, active environmental factors, and crucial safety issues. The conventional methods of risk management, although they partially work, are mostly based on reactive models that are not able to foresee the fast-changing risks like overcrowding, heat stress, and health emergencies among the population. The article suggests the use of AI-driven risk management framework that can allow making predictions, adaptive, and data-driven decisions during the Hajj season. Based on the new research findings on AI-based healthcare, edge computing, digital twins, and intelligent event management, the framework incorporates real-time data collection, prediction based on machine learning, and decision support systems to improve proactive safety and operational resilience. The model promotes early hazard detection, efficient resource distribution, and multi-agency reaction with the help of predictive analytics. Ethical, cultural and data governance is also considered in order to make sure that there is transparency, equity and congruency with the Islamic values. The paper ends by outlining the way forward to deploy AI-enabled safety systems in the future Hajj operations, with cross-sector collaboration, sustainability and learning as its key themes to develop a robust and intelligent pilgrimage ecosystem.