Due to their limited mobility and vocal limitations, paralysed individuals frequently struggle with communication and health monitoring. This work introduces an Internet of Things (IoT)-based system that combines continuous health monitoring with a sen-sor-based smart glove to enhance patient care. The glove detects falls, sends emergency messages via hand gestures, and monitors vital indicators, including SpO2, heart rate, and body temperature. The smart glove uses Arduino and ESP8266 modules with MPU6050, MAX30100, LM35, and flex sensors for these functions. MPU6050 detects falls precisely, while MAX30100 and flex sensors measure gestures, SpO2, heart rate, and body tempera-ture. The flex sensor interprets hand motions as emergency alerts sent via Wi-Fi to a cloud platform for remote monitoring. The experimental results confirmed the superiority and validated the efficacy of the suggested module. Scalability, data logging, and real-time ac-cess are guaranteed by IoT integration. The actual test cases were predicted using a Sup-port Vector Machine, achieving an average accuracy of 81.98%. The suggested module is affordable, non-invasive, easy to use, and appropriate for clinical and residential use. The system meets the essential needs of disabled people, enhancing both their quality of life and carer connectivity. Advanced machine learning for dynamic gesture detection and telemedicine integration is a potential future improvement.