Unveiling Research Trends in Sustainable Finance through AI and Machine Learning: A Bibliometric Approach

Abstract: In recent years, the intersection of artificial intelligence (AI), machine learning (ML), and sustainable finance has emerged as a ground-breaking area of research and application. This study provides a comprehensive bibliometric analysis to investigate the evolving research trends, leading authors, prestigious universities, and key thematic areas in this diverse discipline. By analyzing peer-reviewed publications from major scientific databases spanning 2016 to 2025, the study maps the intellectual structure and development of this field. Results highlight a sharp rise in scholarly output post-2018, driven by global emphasis on Environmental, Social, and Governance (ESG) goals and the growing integration of AI-powered financial decision-making systems. Thematic clustering reveals prominent research areas such as ethical AI, ESG risk assessment, green investment strategies, and AI-enabled sustainability reporting. Additionally, the study underscores the importance of collaborations among academia, financial technology (fintech) firms, and regulatory bodies in advancing sustainable finance innovation. These insights provide valuable direction for future research at the nexus of AI, ML, and sustainable development. Keywords: Artificial Intelligence, Machine Learning, Sustainable Finance, ESG, Bibliometric Analysis