Modern health wearable monitoring technologies are lim- ited as they only give the discrete measures and don’t let you analyze the data in real time. This study introduces Bio-Shield, an AI-powered smart health monitoring jacket engineered to address these constraints by including broad environmental and physiological sensors alongside the real-time anomaly detection. The system consists of sensors for heart rate (MAX30102), ECG (AD8232), blood pressure (MPX5050DP), pulse oximetry, temperature (DS18B20, DHT22), UV radiation (ML8511), gal- vanic skin response (GSR), and electromyography (EMG). Using ma- chine learning an ESP32 microcontroller examines the sensor data to find health related issues like arrhythmias, stress, dehydration and breathing related problems. ECG classification achieves up to 99.724% accuracy using the Random Forest model, while a quantized Multilayer Percep- tron running on hardware achieves 94.8% accuracy with an average in- ference latency of 140 ms. Field tests demonstrate the effective real-time monitoring and automatic temperature regulation under the challenging conditions. Bio-Shield is a major step toward proactive, context-aware healthcare. It delivers smart monitoring for the vulnerable groups such as the elderly, outdoor workers and adventure lovers.