This paper discusses the development of an IoT-based Congenital Heart Disease prediction system to automate the process of predicting cardiovascular diseases. This system is based on sensing various parameters such as the user’s heart rate, blood pressure, and oxygen saturation levels. These parameters are monitored through wireless sensors and integrated with cloud computing services. The system also incorporates Machine Learning methods and algorithms to predict cardiovascular diseases with a high degree of precision. The use of cloud computing services helps to guarantee data security through secure authentication, encryption, authorization, access control, and usage control. This automated system produces personalized risk reports, which enables physicians to track changes in cardiovascular health and provide a personalized plan for preventive care. The effectiveness of this system has been evaluated through various case studies, demonstrating its potential to provide early detection of cardiovascular diseases. This paper highlights the importance of cloud computing services for improving the security and accuracy of predicting cardiovascular diseases. Moreover, it presents a comprehensive overview of system development strategies to further enhance the scalability and accuracy of this system.