Insufficient blood flow to the brain results in a condition known as a stroke, which results in cell death. In worldwide it is currently the leading cause of death. Many risk factors that are suspected to be related to the stroke's origin have been identified through examination of the affected individuals. Using these risk factors, numerous research has been done to predict the disorders linked to stroke. Most models are built using machine learning techniques and data mining. In this study, we used data from medical reports and a person's physical condition to use five machine learning algorithms to identify strokes. We use a substantial number of hospital entries that we have collected. The classification outcome demonstrates that the result is satisfactory and can be applied to real-time medical records in order to address the issues. We think machine learning algorithms can aid in better understanding illnesses and make a useful healthcare partner