This study uses AI tools to explore social media use data and adolescent mental health outcomes. Using a mixed-method analysis of sentiment analysis, natural language processing (NLP) and machine learning algorithms, we examined public social media data from adolescents aged 13–18 years on three major platforms: Instagram, TikTok, and Twitter. The study applied VADER and BERT for sentiment analysis, as well as Random Forest and Support Vector Machine (SVM) algorithms on predictive approaches. The analysis showed that 42% of analyzed posts were negative, compared to a significant increase to 65% in the presence of keywords related to mental health. Children scoring behavioral pattern analysis as both active posters (more than 10 posts per week) but passive consumers were significantly more likely to demonstrate markers of emotional distress. Negative sentiment posts attracted 35% more engagement than neutral or positive content, with the risk of entrenched negative behavioral patterns. The predictive models were able to identify the mental health risk factor with great accuracy, achieving 85% accuracy using Random Forest and 83% using SVM. Analysis over time revealed elevated negativity during periods of high stress such as examination, indicating that looming environmental triggers could contribute to psychological malaise. This study will provide the evidence-based required to formulate early intervention strategies and mental health support systems. This study is the first to use social media behavior to assess in real time if AI tools can monitor and predict risk of mental illness in adolescents, together with the implications for balanced interventions that take into account social media benefits as well as risks.