The elderly population is particularly vulnerable to falls and sudden health deterioration, which can lead to critical consequences if not addressed promptly. This paper presents a smart, wearable-based system designed for real-time fall detection and vital health monitoring. The system uses an MPU6050 sensor to track body motion, and a MAX30100 sensor to monitor heart rate, SpO2 levels, and body temperature. Machine learning model is employed to analyze sensor data and detect anomalies. Amongst the three classification algorithms (KNN, SVM and Random forest) applied, Random forest demonstrated 97% accuracy that is suitable for this application. In case of a fall or abnormal readings, emergency alert along with GPS coordinates is sent to a designated contact through GSM module. A companion web interface visualizes real-time data, aiding in timely medical intervention. The system demonstrates reliability, accuracy, and practicality for enhancing elderly safety and autonomy.