Mental health challenges, particularly anxiety and stress, are prevalent among students due to academic pressures, social expectations, and personal struggles. Traditional mental health support systems often fail to provide timely interventions, leading to severe consequences such as depression or suicidal ideation. This study presents an Early Detection and Support System for Student Mental Health, integrating machine learning models to proactively assess and predict student anxiety levels. The system utilizes data from activity logs, and survey responses to classify students into different anxiety categories and provide personalized support recommendations. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, and Random Forest, were employed to optimize prediction accuracy. The results demonstrate that Logistic Regression achieved the highest accuracy (89.1%) in predicting stress levels, while the Random Forest model performed best in stress reduction prediction. The system's predictive capabilities extend beyond anxiety detection, enabling multi-feature mental health analysis, including depression, self-esteem, and stress levels. By integrating an automated alert mechanism and real-time monitoring, this framework offers a proactive solution for universities to support student mental well-being.