Dehydration and electrolyte derangement constitute critical physiological stressors, particularly in environments characterized by extreme thermal indices and high physical exertion. Despite the clinical significance, continuous monitoring of hydration status remains a challenge due to the lack of non-invasive, cost-effective, and integrated real-time tracking systems. This paper proposes a conceptual Internet of Things (IoT) framework designed for autonomous physiological and environmental telemetry. The proposed architecture leverages an ESP32 microcontroller as a central processing unit, integrating high-fidelity sensors for heart rate (HR), body temperature, ambient humidity, and atmospheric pressure. A novel mathematical Hydration Index (HI) model is developed using min-max normalization and a weighted multi-sensor data fusion algorithm. Unlike traditional reactive measures, this framework proactively estimates fluid-electrolyte status by quantifying the interplay between internal cardiovascular stress and external environmental variables. Initial simulation-based analysis demonstrates the model's logical consistency in differentiating between normal hydration, mild dehydration, and acute risk states. The system architecture emphasizes a layered IoT approach, ensuring high scalability and low-power consumption for integration with mobile health (mHealth) platforms. This research provides a structured foundation for non-invasive health supervision in sports science, geriatric care, and occupational safety. Future extensions will focus on longitudinal experimental validation and the integration of predictive machine learning models for personalized hydration risk assessment.