The high rate of credit default among micro-borrowers in developing economies highlights the limited predictive capacity of traditional risk assessment methods. This study, therefore, aims to predict credit or loan default among micro borrowers in Abeokuta town, Ogun State, Nigeria using STATA-based Least Absolute Shrinkage Selection Operator (LASSO) as a machine learning (ML) programming algorithm. A random sample of 384 microfinance customers was selected for the cross-sectional study employing a simple structured questionnaire as the data instrument. LASSO estimation in STATA 12.1 statistical software at a 5% significance level shows that macroeconomic indicators (inflation and state of economy) and socio-political factors (such as borrower’s income, paid employment status and security) play significant roles in predicting loan default among micro-borrowers. The results produced by the LASSO estimator have higher regression coefficients than traditional logistic regression and perform better. Therefore, the study affirms that the ML programming algorithm provides greater predictive capabilities of credit default among micro borrowers in the metropolitan city of Abeokuta, Nigeria. This finding implies that financial institutions in the study area when leveraging the importance of ML algorithms can be proactive in risk management and optimize their resources through efficient allocation of funds among borrowers. To this end, the study suggests that financial institutions in Nigeria especially microfinance banks should explore the application of ML algorithms for advanced predictive and analytical capabilities of complex patterns of teeming prospective borrowers’ information.