This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models - Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree - within a not-for-profit, four-year institution in the Midwest, the research meticulously processes a dataset from 2013-14 to 2021-22. The analysis reveals the GBM model's superior accuracy, achieving up to 83.3 percent, thereby underscoring its effectiveness in identifying key success factors for adult learners. The findings highlight the significance of age, attendance, and Pell Grant eligibility as critical predictors of academic success, offering actionable insights for educational administrators. This study not only addresses a gap in educational research concerning adult learners but also suggests a strategic focus on these determinants to improve student success rates. By demonstrating the GBM model's predictive capabilities, the research contributes to the broader discourse on employing advanced analytical techniques to enhance educational outcomes, advocating for a more inclusive and data-informed approach to policy development and institutional practice in higher education. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]