This study explored utilizing tree-based machine learning models to identify associations in a range of 107 factors and DUI recidivism among first-time DUI offenders. Three tree-based machine learning models, Decision Tree, Random Forest, and Gradient Boosting were performed on 12,879 first-time DUI offenders during 2013-2017 using a three-year following period, to classify repeat DUI offenders. Study cohorts include 11,651 drivers without recidivism and 1,228 drivers with recidivism. The models tested 107 variables/predictors, including the driver’s demographic factors, drinking behaviors, traffic violations, crash histories, DUI-related violations, social-economic factors, and health and safety factors based on the driver’s residence. oversampling technique was used to balance two classes in the training data in all three models. The top 15-20 predictors were selected from the feature impact analyses of these predictions. Lastly, multiple logistic regression analyses were performed to quantify the effects of selected factors/predictors on the outcome. Among the three models, Gradient Boosting achieved the best predictions on both the original and oversampled datasets. Oversample techniques did improve prediction performances by roughly 10% on the F1 score for Gradient Boosting. Results coalesced around two findings. First, male drivers with higher BAC values, younger age at first DUI citation, whose first DUI citation took place during the weekday, had at least one low-risk citation within three years before first DUI citation, and lived in counties with lower income inequality ratio and higher violent crime rate were more likely to commit a subsequent DUI offense. Second, male drivers who complied with a BAC test upon arrest, whose first DUI citation took place on a weekday, had at least one low-risk citation within three years before the first DUI citation, lived in a county with a lower income inequality ratio, and higher violent crime rate were more likely to commit a subsequent DUI offense. Findings can be used by stakeholders in implementing and improving DUI prevention strategies. The study is limited to a single state, but the comparison of techniques and their shared findings suggest that a multitude and variety of approaches may be appropriate in future impaired driving prevention research.