This bibliometric analysis investigates the relationship between artificial intelligence (AI) and the banking industry by focusing on the transition from conventional to sustainable financial framework in the current context of climate change. This study refines the conceptual framework and examines key findings in the field to document current trends in the literature. It explores new perspectives on the radical shifts within the industry, providing a more nuanced understanding of how AI is influencing the current banking industry. We identified 222 articles, sourced from Scopus, dealing with the application of the interconnected domains of AI, machine learning and deep learning in banking industry by considering both conventional and sustainable frameworks. Our results reveal a significant growth in research on AI methods supporting the transition to sustainable financial frameworks, with a 29.9% annual increase in publications from 2010 to 2023. Key journals such as “Sustainability” and “IEEE Access” play a pivotal role in disseminating influential findings, while authors like Malekipirbazari (2015) exhibit strong citation connections, indicating their lasting impact on the field. The analysis highlights robust international collaboration among leading countries, including India, China, and the United States, and distinct co-authorship networks among researchers. Methodologically, the focus lies on practical applications of AI in finance and the development of advanced techniques like deep learning and decision trees, reflecting the interdisciplinary nature of research at the intersection of AI, sustainability, and finance. The implications of this study underscore the need for continued exploration of AI′s role in sustainable finance, encouraging further collaboration and innovation to address pressing environmental challenges.