Asian Journal of Geographical Research

The comprehensive approach to bibliometric analysis examines global research on Early Warning Systems (EWS) in relation to disaster management from 2001 to 2024, with data retrieved from the Web of Science and Scopus databases. The Bibliometrix R package is the tool that is used to examine publication trends, influential sources, country-level contributions, collaboration networks, and thematic evolution to map the intellectual and structural development of the field. It helps to reveal a steady and exponential growth in publications, particularly after 2015, coinciding with the adoption of the Sendai Framework for Disaster Risk Reduction (2015–2030). China takes the lead in research productivity with the largest share of publications followed by Italy, the United States, Australia, and Canada that have higher citation impact and international visibility. Natural Hazards, Landslides, and the International Journal of Disaster Risk Reduction are identified as the top research outlets and the developing conceptual basis in the field is comprised of keywords associated with “early warning system,” “disaster management,” “floods,” “risk assessment,” and, “climate change." The thematic and factorial analyses depicted early warning systems, climate change, and disaster management as well-developed motor themes, while emerging themes in the research literature included machine learning, resilience, and Internet of Things (IoT) based warning models. Additionally, the international collaboration network suggests a dual-core pattern with China leading in South-South partnerships, and Europe anchoring research networks in the West. The authors highlight that EWS research has developed as a multidisciplinary and globally located compendium of early warning system science, that connects technology innovation, environmental science with policy-developed frameworks, to improve resilience to disasters. Findings from this study suggest the importance of strengthening cross-regional collaboration, strengthening open data integration and developing AI- and IoT-enabled early warning systems to build more adaptive and climate-resilient societies.