The ability to foresee the success or failure of start-ups is essential for investors, policymakers, and stakeholders. This study attempts to evaluate the performance of Random Forest in predicting business outcomes and rule extraction. The Random Forest model integrated 66,369 cases with 923 rows and 49 mixed quantitative and qualitative attributes. The study also intends to compare its findings with five recent studies (2020–2025) to measure the accuracy of Random Forests in predicting failure or success of business start-ups. It illustrates the role of non-financial operational or geographic factors and, through straightforward decision rules, challenges the conventional use of financial ratios to provide non-ambiguous decision-making statements. The results contribute to the understanding of the role of predictive analytics in financial decisions and the application of machine learning in this field.