The abrupt, potentially fatal stop of heart action is known as cardiac arrest. Predicting cardiac arrest early on is crucial because it gives you time to take the appropriate precautions or act quickly when it starts. Big data and artificial intelligence (AI) technologies are increasingly employed to improve the capacity to anticipate and prepare for patients in danger. The objective of this research is to explore the implementation of AI tools for the prediction of cardiac arrest. To achieve the desired results, this study has used Rough Set Theory (RST), one of the significant tools to handle vague and imprecise data; this study has been divided into three categories: category 1 includes the application of RST reduct algorithm to find the significant attribute, category 2 includes an AI warming technique using several studies. Several studies have also been carried out to forecast future cardiac arrest in category 2, and the third category classifies the healthy person as a person suffering from cardiac arrest. The last section of this study used statistical techniques to validate the claim.