مجلة الاربعين المحكمة
In such high-density contexts, even brief delays in information processing can trigger critical bottlenecks, underscoring the urgent need for real-time intelligent disaster management solutions. This research proposes an intelligent predictive framework for Disaster Management in such large human populations by integrating artificial intelligence (AI) and the Internet of Things (IoT) technologies into a multi-level system (AIoT) This framework consists of a network of wearable sensors, drones, and intelligent weather stations, which transmit data in real-time to terminals supported by computer vision algorithms and graphical neural networks (GNN), capable of monitoring intensity spikes and panic patterns in less than 250 milliseconds. This system enhances performance through the Edge AI layer, which performs processing operations locally while delegating redundant tasks to cloud computing within a decentralised three–tier structure (Edge–Fog–Cloud, while delegating redundant tasks). This structure has effectively reduced emergency response time, protecting lives and providing rapid support at major mass events. Its effectiveness in reducing emergency response time is also evident, as seen during its efficacy in reducing emergency response time, which directly contributes to the visit of the Arbain Pilgrimage.