Adolescent mental health has become a critical public health concern, with increasing rates of depression, anxiety, and suicide among youth. The early identification of mental health risks is essential for timely intervention, yet traditional clinical approaches often fall short in detecting early warning signs, especially in underserved populations. This research explores the use of transformer-based deep learning models, such as BERT and RoBERTa, to analyze social media datasets for the early detection of adolescent mental health challenges. Social media platforms serve as digital diaries where young individuals often express emotions, distress, and behavioral changes, making them valuable sources for unobtrusive mental health monitoring. The study leverages annotated datasets derived from platforms like Reddit and Twitter, focusing on posts by adolescents or those discussing youth mental health. A multi-class classification framework is implemented to categorize posts into mental health risk categories, including depression, anxiety, suicidal ideation, and normal behavior. The transformer models are fine-tuned and evaluated against traditional machine learning baselines such as SVM and logistic regression, showing significantly higher accuracy, precision, and recall in detecting nuanced emotional and psychological signals. This approach aligns with the strategic goals of the Centers for Disease Control and Prevention (CDC) and the National Institutes of Health (NIH), which emphasize the importance of leveraging digital tools and AI for proactive youth mental health initiatives. Ethical considerations, including data privacy, informed consent, and bias mitigation, are addressed to ensure the responsible deployment of AI in sensitive health contexts. The findings suggest that transformer-based models can serve as effective early-warning systems, flagging at-risk individuals for further evaluation or support. By integrating this technology into school systems, mental health hotlines, or community outreach programs, stakeholders can enhance preventative care and reduce the long-term impact of adolescent mental health disorders. The study underscores the transformative potential of AI in safeguarding vulnerable youth and calls for further interdisciplinary collaboration to scale such innovations.