In the past few years, heart disease has become the foremost worldwide contributor to mortality. This ailment, with a profound effect on the functioning of the heart, leads to issues such as infections in the coronary arteries and diminished blood vessel performance. These complications can culminate in severe unlikely events like heart attacks and strokes. In India alone, approximately one person succumbs to heart disease every minute. To curb the fatalities stemming from cardiac disorders, there is an urgent need for a swift and efficient detection strategy. IoT sensors are utilized in conjunction with Machine Learning (ML) and Deep Learning (DL) techniques to identify heart disease. In this research, we have successfully applied IoT devices and a sensor network to detect heart diseases. This study introduces a medical IoT device designed to gather heart data from patients both before and after the onset of heart disease. This continuously transmitted data is processed using RBF, MLP, and Bi-LSTM models for predicting heart disease. The deep learning approach utilizes past analyses to learn critical features related to heart disease, achieving efficiency in handling complex data. After conducting a series of experiments, we evaluate the system’s performance using metrics such as f-measure, sensitivity, specificity, loss function, and Receiver Operating Characteristic (ROC) curves. The HDRBi-LSTM method, in combination with IoT-based analysis, achieves an impressive accuracy rate of 99.5% with minimal time complexity (5 s), effectively reducing heart disease mortality by simplifying the diagnosis of this condition.