Internet-of-Things and machine learning are paving the way for a new era in healthcare. Physiological data can now be collected from anyone, anywhere, at any time through innovative technologies such as wearable and implantable medical devices (IWMDs). By using this capability, routine and clinical settings can be analyzed for patterns and predictions of healthcare outcomes, extending the reach of healthcare beyond traditional clinical settings. With these advancements, passive data collection is replaced with proactive decision-making, thereby improving patient care to a great extent. The sensors analyze vital parameters like blood pressure, temperature, heart rate, oxygen levels, and brain activity in an Internet of Things-based comatose patient monitoring system. The data is gathered and operated by an embedded microcontroller before being transmitted over Wi-Fi to a cloud platform. Real-time warnings and notifications of any unusual behaviors are sent to healthcare practitioners via web dashboards or mobile apps. To increase prediction and recommendation accuracy and guarantee prompt interventions, the system employs machine learning methods. This ongoing observation improves patient care and offers a dependable way to manage comatose patients' healthThese technologies are an enormous breakthrough in healthcare since they enable constant patient monitoring and early detection of prospective problems. By facilitating rapid detection and treatment for a variety of medical diseases, this not only boosts the standard of care for people in critical circumstances but also has the ability to improve public health in general. As these technologies develop further, they have the potential to completely transform the healthcare system by increasing the effectiveness and availability of high-quality care.