Objective: The current study sought to clarify and harness the incremental validity of emotional dysregulation and unawareness (EDU) in emerging adulthood, beyond ADHD symptoms and with respect to concurrent classification of impairment and co-occurring problems, using machine learning techniques. Method: Participants were 1,539 college students (M[subscript age] = 19.5, 69% female) with self-reported ADHD diagnoses from a multisite study who completed questionnaires assessing ADHD symptoms, EDU, and co-occurring problems. Results: Random forest analyses suggested EDU dimensions significantly improved model performance (ps < 0.001) in classifying participants with impairment and internalizing problems versus those without, with the resulting ADHD + EDU classification model demonstrating acceptable to excellent performance (except in classification of Work Impairment) in a distinct sample. Variable importance analyses suggested inattention sum scores and the Limited Access to Emotional Regulation Strategies EDU dimension as the most important features for facilitating model classification. Conclusion: Results provided support for EDU as a key deficit in those with ADHD that, when present, helps explain ADHD's co-occurrence with impairment and internalizing problems. Continued application of machine learning techniques may facilitate actuarial classification of ADHD-related outcomes while also incorporating multiple measures.