The machine-learning (ML) approach in this research—primarily the random forest classifier—demonstrates the usefulness of large, multivariable models in identifying complex behavioral patterns behind treatment seeking in AUD. The model highlights that clinical and behavioral indicators, particularly those capturing severity and consequences of alcohol use, are far more informative for prediction than genetic risk scores. This suggests that in settings where detailed psychological and medical histories are available, ML models can help identify individuals who are likely to seek or avoid treatment, which could guide early intervention strategies. Additionally, the modest contribution of PGS shows that while genetic data may be helpful for early-life risk assessment (especially in younger individuals), it is not yet clinically actionable for predicting treatment engagement. The study underscores that machine-learning models combining rich phenotypic, environmental, and clinical data may be a more practical and impactful tool for improving AUD treatment outreach, at least until genetic datasets become larger, more diverse, and more predictive.