This project develops a wearable-compatible system that enables continuous, noninvasive metabolic health monitoring by analyzing sweat biomarkers. It combines electrochemical impedance spectroscopy (EIS) hardware with a robotic platform that generates controlled artificial sweat samples to produce high-quality training data. Instead of relying on fragile chemical coatings, the system uses machine learning to classify electrolyte compositions from impedance spectra. By integrating this approach with smartwatch-compatible components, the project demonstrates a scalable, reusable method for biochemical sensing. Overall, it bridges the gap between conventional wearable devices and real-time metabolic insight.