Abstract

Contemporary organizations navigate increasingly volatile and complex risk environments characterized by ongoing cyber threats, rising climate-related disruptions, and unprecedented technological changes, rendering traditional risk management frameworks inadequate and conceptually outdated. The evolving complexities necessitate more flexible, data-driven, and proactive strategies to effectively address the dynamic nature of contemporary risk. This work investigates the evolving role of Artificial Intelligence (AI) and Machine Learning (ML) in improving risk management techniques. A bibliometric analysis approach based on the PRISMA framework is used to thoroughly examine a corpus of 806 peer-reviewed publications in the Scopus database. Using advanced software, including VOSviewer and Biblioshiny, we analyzed co-authorship patterns, co-citation networks, and keyword co-occurrences to provide a comprehensive picture of the intellectual framework of this multidisciplinary topic. The findings show a significant growth in academic interest since 2020, driven by global turmoil and the need for agile, data-driven solutions. Leading contributions are focused in China, the United States, and the United Kingdom, with notable institutional involvement from the University of Technology, Sydney. The results underscore a paradigm shift: AI and ML are no longer peripheral tools but are increasingly integral to strategic risk governance. The study recommends expanding interdisciplinary collaboration and promoting the application of AI and ML in underexplored sectors to enhance resilience and mitigate future uncertainties.