In just ten years, the prevalence of stroke in people has grown by 45%, with heart disease being the main contributing factor. An estimated 1.28 billion persons in the 30–79 age range are projected to have heart disease. Various studies have concentrated on different factors like age, gender, kind of employment and many other parameters; nevertheless, a primary cause of stroke is an imbalance of lipids, including triglycerides, HDL, LDL, and many others, which are directly related to heart disease. Utilising an open-source medical dataset with enormous patient data, the study provided a thorough examination of how contemporary machine learning approaches use the data for the prediction of heart disease with its impact on stroke. In this study, artificial neural networks (ANN), neural collaborative filters (NCF), and eXtreme Gradient Boosting (XG Boost) are used to predict brain stroke and show interesting findings.