One of the main concerns for hospitals in developing nations is a compliant patient-concerned system. Due to the lack of appropriate, accessible, and ascendable smart technology, the majority of hospitals in developing countries lack sufficient health assistance. The objective of this project is to generate a functional system that will enable hospitals to provide real-time feedback to patients in need of assistance. The central idea of this research is patient cardiac disease prediction using machine learning (ML). The raised area utilized for this study to accumulate and manage data and ML paradigms is IBM Cloud, IBM Watson Studio. Bagging approach of ensemble learning method has been applied to improve the paradigms accuracy. For ensemble learning, the following algorithms are employed: Bagging SVC, K-Neighbors, Extra Trees, and Random Forest.