A stroke is a medical condition characterized by an interruption to the flow of blood and essential nutrients to the brain, resulting in damage to brain blood vessels. According to data from the World Health Organization, a brain stroke ranks as the primary cause of both global disability as well as death. Timely identification of the same can significantly mitigate its impact. In our research project, we seek to investigate the methodologies employed by machine learning in predicting the occurrence of strokes. We have conducted a comprehensive review of prior studies and assessments to gain insights into the various machine learning techniques employed for the prediction of a heart stroke. A majority of these studies focused on using mortality rates and functional outcomes as key predictors. Among the most frequently utilized machine learning methods were support vector machines, decision trees, and neural networks.