This paper presents a novel electronic skin (CARES) for non-invasive monitoring of stress responses. The CARES platform integrates multiple sensors to monitor vital signs (pulse waveform, galvanic skin response, and skin temperature) and sweat-based biomarkers (glucose, lactate, uric acid, sodium ions, potassium ions, and ammonium). The sensors are fabricated using a scalable inkjet-printing approach and are designed to provide long-term stability and high sensitivity. The CARES device can be worn continuously for 24 hours during various daily activities, allowing for dynamic stress response monitoring. Machine learning models, specifically an Extreme Gradient Boosting (XGBoost) model, are used to classify stressors and predict state anxiety levels with high accuracy (98.0% for stressor classification and 98.7% for anxiety level prediction). The CARES platform demonstrates the potential for advanced wearable multimodal physicochemical monitoring in healthcare and human performance monitoring.This paper presents a novel electronic skin (CARES) for non-invasive monitoring of stress responses. The CARES platform integrates multiple sensors to monitor vital signs (pulse waveform, galvanic skin response, and skin temperature) and sweat-based biomarkers (glucose, lactate, uric acid, sodium ions, potassium ions, and ammonium). The sensors are fabricated using a scalable inkjet-printing approach and are designed to provide long-term stability and high sensitivity. The CARES device can be worn continuously for 24 hours during various daily activities, allowing for dynamic stress response monitoring. Machine learning models, specifically an Extreme Gradient Boosting (XGBoost) model, are used to classify stressors and predict state anxiety levels with high accuracy (98.0% for stressor classification and 98.7% for anxiety level prediction). The CARES platform demonstrates the potential for advanced wearable multimodal physicochemical monitoring in healthcare and human performance monitoring.