This paper underlines the significant transformations that recent applications of Internet of Things (IoT) technologies bring in an approach towards real-time health monitoring and disease prediction. This paper describes an Internet-based platform, namely, Vital Care, based on IoT sensor technologies that continuously monitor vital signs and perform predictive disease diagnosis. The system uses several sensors for Vital Care. These include the DHT11 sensor, which is designed to collect information on temperature and humidity conditions, LM35 temperature sensor, blood pressure sensor, and hearth rate sensor. All these devices collect vital physiological information that serves as the foundation for any type of analysis of real-time patient health. Randomized Forest algorithm serves to power the diagnostic strength of the platform by analyzing sensor data to predict the possibility of various diseases based on important health factors. The system has been put through a series of tests on real datasets and has shown remarkable improvements in diagnostic accuracy and dependability over traditional techniques, particularly in managing intricate datasets of multiple health markers. Thus, this data-driven method was implemented for accessible and effective illness prediction by deploying it as a web application using the Flask API in integration with IoT-enabled continuous monitoring. The proposed system is a huge leap for healthcare in that It can exert an effect affect early diagnosis and intervention in the life of patients, thereby improving patient outcomes as well as providing a scalable predictive diagnostic solution.