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Microfinance institutions in Kenya play a unique role in promoting financial inclusion, loans, and savings provision, especially to low-income individuals and small-scale entrepreneurs. However, despite their benefits, most of their products and programs in Machakos County have been reducing due to repayment challenges, threatening their financial ability to extend further credit. The objective of the research was to establish key credit risk indicators for microfinance institutions operating within Machakos County, Kenya. The study adopted a mixed research design using supervised machine learning approach. It randomly sampled 6771 loan appli- cation account records and repayment history.Rstudio and Python programming languages were deployed for data pre-processing and analysis. The logistic regression algorithm, XG Boosting and the random forest ensemble method were used to rank the feature importance. Based on the study findings; The amount of loan required, the income level, the gender and the age of the applicant were the main features that influenced loan default rate. Integration of the hard and soft data into machine learning for better credit risk assessment outcome was recommended. Similar research but using different target population and institutions, to as- certain the validity, reliability and the generalizability of the study findings was recommended for further research.