The Internet of Things (IoT) and machine learning are transforming healthcare by enabling real-time physiological data collection through wearable and implantable medical devices (IWMDs). This innovation allows healthcare providers to analyze routine and clinical settings, identify patterns, and predict health outcomes beyond traditional medical environments. IoT-based comatose patient monitoring systems utilize sensors to track vital parameters such as blood pressure, temperature, heart rate, oxygen levels, and brain activity. The collected data is processed by an embedded microcontroller and transmitted through Wi-Fi to a cloud platform, where healthcare providers receive real-time alerts through web dashboards or mobile applications. By integrating machine learning techniques, the system enhances prediction accuracy and enables timely interventions, replacing passive data collection with proactive decision- making, thereby improving patient care. These technologies mark a significant breakthrough in healthcare, offering continuous patient monitoring and early detection of complications. By facilitating rapid diagnosis and treatment for various medical conditions, they enhance the standard of care for critically ill patients and have the potential to improve public health on a broader scale. As these innovations continue to evolve, they may revolutionize healthcare by increasing both the efficiency and accessibility of high-quality medical care.