In military operations, real-time monitoring of soldiers' health is essential for ensuring mission success and safeguarding personnel, yet such systems face challenges related to accuracy, security, and resource efficiency. This research addresses the critical need for secure, real-time monitoring of soldier vitals in the field, where operational security and performance are paramount. The paper focuses on implementing a machine-learning-based system capable of predicting the health states of soldiers using vitals such as heart rate (HR), respiratory rate (RESP), pulse, and oxygen saturation SpO