Heart disease is a disease that effects on the function of heart. There are number of factors which increases risk of heart disease. At the present days, in the world heart disease is the main cause of deaths. The World Health Organization (WHO) has expected that 12 million deaths occur worldwide, every year due to the heart diseases. Prediction by using data mining techniques gives us accurate result of disease. IHDPS (intelligent heart disease prediction system) can find out and extract hidden knowledge related with heart disease from a historical heart disease database. It can answer complex queries for diagnosing heart disease and thus help healthcare analysts and practitioners to make intelligent clinical decisions which conventional decision support systems cannot. A few kinds of heart disease are cardiovascular diseases, heart attack, coronary heart disease and Stroke. Stroke is a type of heart disease; it is caused by narrowing, blocking, or hardening of the blood vessels that go to the brain or by high blood pressure. System based on the risk factors would not only help medical professionals but also it would give patients a warning about the probable presence of heart disease even before he visits a hospital or goes for costly medical Checkups. Machine learning will help in predicting and making decisions from the large amount of data produced by healthcare industries and hospitals. We have also seen Machine Learning techniques are being used in many fields in different areas. It discovered a new method that will help in finding significant features by applying machine learning techniques such as Random Forest algorithm that results in improving the accuracy in the prediction of cardiovascular disease. The prediction model will contain different types of machine learning algorithms.