The heart is the subsequent major organ connecting to the brain, which has stronger precedence in the human evidence. It elevates the blood and accumulations to all devices of the entire body. Forecast of circumstances of heart conditions in the medical field is important to work. Data analytics is beneficial for divining more knowledge, and it helps the medical centre predict various conditions. A huge number of patient-related data is prepared each month. The collected data can be beneficial for the source of predicting the emergence of future weaknesses. Unusual data mining and machine learning procedures have been used to indicate heart disease. This research proposed heart disease prediction using various modified Recurrent Neural Network (mRNN) deep learning algorithms. Numerous feature extraction and selection methods have been used to get important features and data collection using custom-generated IoT environments. The system effectively provides heart risk scores with the highest accuracy in a runtime environment.