Mass human gatherings—such as the Maha Kumbh Mela in Prayagraj, India, and the annual Hajj pilgrimage in Makkah, Kingdom of Saudi Arabia—represent unprecedented challenges in crowd safety, emergency logistics, and public administration. In the hyper-connected digital era, the primary operational bottleneck in managing such mega-events has transitioned from physical crowd dynamics to digital information contagion. Misinformation, unverified panic rumors, and malicious disinformation circulating via encrypted messaging channels (e.g., WhatsApp) and open microblogging platforms (e.g., X/Twitter) can trigger catastrophic stampedes, strain emergency healthcare services, and erode public trust in state authority.

This study proposes an integrated, multi-modal Artificial Intelligence (AI) framework combining cross-lingual Natural Language Processing (NLP), dynamic spatio-temporal sentiment tracking, and automated Retrieval-Augmented Generation (RAG) models for real-time crisis communication. Analyzing an empirical dataset of 4.82 million multi-lingual social media posts, public channel messages, and encrypted network telemetry across Hindi, Arabic, English, Urdu, Bengali, and regional dialects, this paper evaluates the velocity, viral mechanics, and psychological drivers of digital panic.