Mental health is a crucial factor influencing overall human well-being, and in the digital age, psychological vulnerabilities have increasingly become a critical human attack surface for cyber threats. Individuals experiencing mental health disorders such as depression or anxiety are demonstrably more susceptible to targeted cyber exploitation, including social engineering, phishing, and digital coercion. In response to this intersection of psychological well-being and human-centric cybersecurity, this study explores the use of social media data for the prediction and classification of mental health conditions to identify vulnerable populations, alongside the development of a secure AI-assisted support system. We analyze and combine three datasets to construct a unified dataset of 14 classes, enabling fine-grained risk assessment for conditions including depression, anxiety, and suicidal ideation. For classification, Logistic Regression significantly outperforms Multinomial Naive Bayes, achieving an accuracy of 95% compared to 74%. To bridge the gap between detection and intervention, the system integrates a conversational module powered by LLaMA 2 (7B). Activated when severe risk is detected, this module provides context-aware interaction to support individuals and mitigate their vulnerability to both psychological crisis and digital exploitation. Deployed via Streamlit, this research serves as an assistive tool for professionals, highlighting the potential of combining machine learning with conversational AI to secure the human element against multifaceted digital and cognitive threats.