This study presents a novel approach to deal with prototyping a smart healthcare IoT framework for precise fall detection in older adults using multi-sensor data fusion. The proposed framework utilizes advanced filtering and context-aware analysis to enhance the accuracy of fall detection, addressing key challenges in current systems such as high false positives and slow reaction times. A MPU6050 accelerometer and gyrator, incorporated with an ESP32 microcontroller,is used to distinguish falls while limiting false-alarms precisely. The framework utilizes Blynk, an IoT platform, to work with continuous observing and warnings. The methodology includes gathering information from the MPU6050 sensor, handling it with the ESP32, and utilizing AI calculations to separate falls from different exercises of everyday living. The system includes coordinating wearable sensors with lightweight AI calculations to guarantee continuous location and similarity with low-power gadgets. The discoveries exhibit improved reliability, sensibility, and real-world applicability of the system in elderly care settings. This research has critical ramifications for improving the wellbeing and freedom of older adults by giving convenient mediation in case of a fall.