According to the World Health Organization, stroke is a leading cause of death worldwide and is an issue that needs a lot of attention. It is a disease that is best prevented and generally can be by making diet changes and regularly monitoring blood pressure. This study investigates some methods by which stroke can be prevented and diagnosed. It went through the traditional methods of prediction, machine learning, and deep learning methods. Five machine learning algorithms were applied on an existing open-source dataset and tested for accuracy. The algorithms include logistic regression, random forest, decision tree, K-nearest neighbour, and support vector machine. At the end of testing, the prediction system was deployed as a simple web application with a Random Forest model using appropriate programming languages. The dataset can be found at this site: https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset/download?datasetVersionNumber=1