Sustainability and risk management in microfinance institutions (MFIs) have become increasingly important since the global financial crisis, with a continual emphasis on how risks are identified, quantified, reported, and controlled. Machine learning is increasing in business contexts, with numerous solutions already in place and a lot more being investigated. The application of machine learning has also been suggested as a project that could aid in the modernisation of risk management functions of MFIs on a large scale. The present study employs a comprehensive bibliometric analysis to better understand machine learning applications for MFIs in the Indian context. Data collection from the Scopus database has been modified from 2012 to 2023 (until December 22, 2023). A dataset of 32 papers was collected from the Scopus database using a modified search. The R language and bibliometrix are used in this paper to perform bibliometric analysis and science mapping. According to the study’s findings, there has been a substantial increase in publications from 2017 onwards. We specifically found an overall growing tendency since 2017, with minor changes in recent years; nonetheless, it has continued to rise in general. Overall, an increase in the average number of citations has been visible from 2018 to now. “Finance” and “machine learning” have occurred the maximum number of times (11 times). The University of Malaya has shown increased affiliations’ production over time from 2015 onwards.