Artificial intelligence applications in infectious disease surveillance and outbreak prevention at mass gatherings: The Hajj experience

Mass gatherings pose substantial challenges for infectious disease surveillance and outbreak prevention due to extreme crowd density, international travel, and operational complexity. Traditional surveillance systems, while effective for retrospective analysis, may lack the timeliness required for early outbreak detection and real-time decision-making during large-scale events. The Hajj pilgrimage, the world’s largest recurrent mass gathering, provides a unique and well-established setting for evaluating innovative public health surveillance strategies. This review aims to examine current and emerging applications of artificial intelligence (AI) in infectious disease surveillance and outbreak prevention at mass gatherings, with a specific focus on the Hajj experience, and to identify transferable lessons relevant to global mass gathering medicine. A narrative review of the literature was conducted using PubMed/MEDLINE, Scopus, and Web of Science, covering publications from January 2000 through February 2026. The review included studies, reviews, modeling analyses, and authoritative public health reports addressing AI-enabled surveillance, digital health tools, and outbreak prevention strategies relevant to mass gatherings. Evidence was synthesized thematically according to key domains of AI application. AI-enabled approaches—including syndromic surveillance, digital contact tracing, crowd density analytics, thermal screening, and predictive modeling—have enhanced real-time situational awareness and early outbreak detection during mass gatherings. Experience from the Hajj demonstrates the value of integrating heterogeneous data streams through centralized command-and-control systems, enabling proactive public health responses and optimized healthcare resource allocation. Despite these advances, challenges related to data governance, interoperability, workforce readiness, algorithmic bias, and public trust remain significant barriers to widespread implementation. Artificial intelligence has the potential to substantially strengthen infectious disease surveillance and outbreak prevention at mass gatherings when integrated within robust public health governance frameworks. The Hajj serves as a scalable, real-world model for AI-enabled surveillance and a functional prototype for preparedness against emerging threats, including “Disease X.” Lessons derived from this setting are broadly applicable to other mass gatherings and contribute to strengthening global health security. • Artificial intelligence enhances real-time infectious disease surveillance at mass gatherings • The Hajj pilgrimage provides a scalable model for AI-enabled outbreak prevention • AI-supported syndromic surveillance improves early detection and situational awareness • Digital contact tracing and predictive modeling support proactive public health responses • Ethical governance and workforce readiness are essential for responsible AI deployment