Mental health issues among stressed workplace personnel (e.g., PTSD, depression, stress) often go under-detected and under-treated. Existing healthcare systems lack real-time, data-driven support to predict, monitor, and intervene before conditions worsen. This creates operational risks in readiness and long-term healthcare costs. The main objective of this project is explore how AI and data science techniques (e.g., machine learning, natural language processing, predictive analytics) can identify mental health risks in stressed workplace personnel, investigate the integration of wearable devices, electronic health records, and survey data for early detection, and propose a framework that balances accuracy, privacy, and ethical concerns in the workplace and healthcare applications. The research approach includes data sources such as public health datasets, simulated different scenarios, literature review, and anonymized persons health data, tools python, pandas, tensor flow/pytorch, and statistical analysis in R/Excel. expected results is a scalable AI framework that can be adapted for both military and civilian healthcare, contribution to national security and public health policy discussions, and strengthen the university’s visibility in AI-driven healthcare and defense applications.