A Scopus-based scientometric and conceptual mapping study of Artificial Intelligence and machine learning in ESG and sustainable finance

Artificial intelligence (AI) and machine learning (ML) integrated with a broader environmental, social, and governance (ESG) framework are a cutting-edge topic in the realm of sustainable finance academic research. This study conducts a comprehensive bibliometric and science mapping analysis of 1,083 journal articles and reviews published between 2001 and 2025. The three primary research objectives guiding our investigation included: (1) Who are the most highly influential contributors to this area? (2) What are the growing collaborations within the academic publishing community? and (3) What are the most prominent academic research themes emerging at this point? The Analysis is done using Bibliometrix/Biblioshiny packages using R and VOSviewer software. We employed performance analysis, co-authorship network analysis, bibliographic coupling, keyword co-occurrence mapping, thematic analysis, and Multiple Correspondence Analysis (MCA). This bibliometric analysis illustrates that from 2019 onward, there has been an exponential increase in research publications, reaching a total of 442 published articles in the year 2025. Science mapping identifies distinct research clusters, including AI applications in ESG performance measurement, digital transformation, green finance, and climate analysis. However, the themes relating to CSR and Governance are less deeply or uniformly integrated into the growth of AI/ML research and therefore remain theoretical constructs existing in an isolated state without a broad general applicability across multiple areas of finance technology development. The findings of this study provide evidence of the rapid advancement of this area of sustainable finance research.