Mapping the Intellectual Structure of AI ‐Driven ESG Cost Modeling: A Bibliometric Analysis of Trends, Themes, and Future Directions

ABSTRACT The bibliometric and qualitative analyses of 470 publications in the Web of Science Core Collection from 2004 through 2025 in this study associated the intellectual structure and thematic evolution of AI‐driven ESG cost modeling. The research commences using an integrated analytical framework consisting of three tools: (1) Bibliometrix (R) for answering publication trends; (2) VOSviewer to answer influential contributors to the field and conceptual themes; and (3) NVivo to answer intellectual foundations of a field. The fallouts show a robust growth in the research since 2018, where the use of AI for cost prediction, the evaluation of ESG performance, the digitalization of sustainability reporting, and the use of AI in governance‐based decision making are gradually relevant. There is still a conceptual disconnection, however, that needs to be bridged; theoretical integration is limited and a lack of attention has been paid to supporting the use of AI and standardized ESG cost modeling frameworks. This study combines bibliometric science planning with a qualitative thematic analysis to build the understanding of how AI can contribute to the decision‐making process for ESG‐oriented organizations and to better understand the emerging research priorities. The findings have known implications for researchers, managers, and policy makers who want to create clear, accurate, and goal‐directed AI‐supported ESG cost modelling systems for sustainable organizational performance.