This Paper offers a web-based interface that uses machine learning methods to determine a person’s risk of brain stroke. In order to forecast the likelihood of a stroke, the system gathers critical health-related data, including age, blood pressure, glucose level, history of heart disease, body mass index, smoking habits, and lifestyle factors. To analyse medical data and increase prediction accuracy, a variety of machine learning algorithms are used, such as Support Vector Machine, Decision Tree, Random Forest, and Logistic Regression. Users can enter their health information on the web application’s user-friendly platform and receive the results of their stroke risk assessment right away. By helping people and medical professionals recognise high-risk conditions before serious complications arise, the suggested system promotes early detection and preventive healthcare. The system is appropriate for real-time healthcare monitoring and decision support applications because experimental evaluation shows that it achieves dependable prediction performance with increased accuracy and efficiency.