Falls among elderly individuals are the highest concerns in public health, ranging from severe injuries to further hospitalization and leading to a degraded quality of life. The majority of conventional fall detection systems rely on accelerometer data to generate post-fall notifications, which may result in a delay in the timely assistance. Active identification and prevention must be implemented to reduce the risks associated with falls and improve elderly care. This paper proposes a novel wearable device integrated with Internet of Things (IoT) technology to detect both pre-fall and fall events. The system would monitor the physiological and movement patterns using SpO₂ sensors and accelerometers for real-time alerts and also monitor the environmental temperature and glucose level as additional features. The wearable device consists of a microcontroller ESP8266, which collects data from sensors and then sends it over IoT to an Android application. This system is integrated with the Firebase database, which can be deployed for real-time data acquisition and monitoring. It sends alerts during abnormalities that can be shared with family members or caregivers, providing preventive health care. As a result, early testing shows that the device can accurately identify abnormal peripheral capillary oxygen saturation levels and movement patterns, thus providing alerts with high accuracy. The proposed system offers a comprehensive, cost-effective method of improving fall prevention among the elderly by further enhancing their safety and facilitating the work of caregivers to help prevent incidents on time.