The move from childhood to adulthood and university is rife with challenges like stress, anxiety, and depression, which can impact academic achievement and overall health. Key stressors are financial difficulties, fear of failing, overuse of technology, use of alcohol or other substances, and academic pressure. AI-based models like Random Forest and Logistic Regression have proved to be successful in detecting and analyzing mental health issues of college students. Random Forest performs best even with diverse datasets, while Logistic Regression works best with structured, smaller datasets. Research with big data shows the early warning and intervention capacity of AI on students’ mental health. The study presented the findings that machine learning methods provided fine predictive ability, and also that the most important predictors were finance, campus belonging, disability, and age. The models didn’t disappoint in terms of the prediction of the disorders due to the AUC, which reached 0.74 for anxiety and 0.77 for depression.