Cardiovascular disease is a leading cause of death worldwide. With nearly one person dying from heart disease every minute in today's ordinary contemporary life, the issue has emerged as one of the most pressing problems. Predicting the start of diseases at an early stage is critical nowadays. When used to healthcare, machine learning can accurately and swiftly identify illness. This study evaluates prospective advancements in the treatment of heart illness. The datasets utilised contain medical parameter characteristics. The samples were analysed in Python by means of the ML procedure, specifically the Forest Algorithm. This approach, which analyses patient data from the past to anticipate future deaths, reduces the incidence of fatalities. The Random Forest technique, a powerful Machine Learning tool, is used in this research to construct a reliable heart disease prediction system. The data is read from a CSV file containing patient records. The procedure is carried out after obtaining the information, and the effective cardiac arrest level is created. The proposed approach has the following advantages: it is very customizable, has a high success rate, and provides excellent reliability and efficiency.