This paper presents a machine learning-based stroke predictor application designed to identify stroke risk factors and predict the likelihood of stroke. The study uses a dataset containing various medical variables, including hypertension, heart disease, age, and sex, to train machine learning algorithms such as Random Forest, Decision Tree, and SVM. The dataset was preprocessed to handle missing values and then divided into training and test sets. The algorithm with the highest accuracy was selected for the prediction model. The system aims to help patients determine their stroke risk and ensure they receive appropriate medical attention. The proposed system includes a user-friendly graphical user interface (GUI) that allows users to input their medical information and receive a stroke risk prediction. The system uses classification algorithms to analyze the input data and predict the likelihood of stroke. The results show that the system can achieve an accuracy of up to 94.30% in predicting stroke risk. The study also discusses the importance of stroke prediction in healthcare and highlights the potential of machine learning in improving stroke diagnosis and treatment. The system is designed to be cost-effective and efficient, providing personalized warnings and lifestyle advice to reduce stroke risk. Future work includes improving the model's accuracy by using more data and enhancing the system's functionality to provide better user experience. The system is expected to contribute to the development of more effective stroke prevention strategies.This paper presents a machine learning-based stroke predictor application designed to identify stroke risk factors and predict the likelihood of stroke. The study uses a dataset containing various medical variables, including hypertension, heart disease, age, and sex, to train machine learning algorithms such as Random Forest, Decision Tree, and SVM. The dataset was preprocessed to handle missing values and then divided into training and test sets. The algorithm with the highest accuracy was selected for the prediction model. The system aims to help patients determine their stroke risk and ensure they receive appropriate medical attention. The proposed system includes a user-friendly graphical user interface (GUI) that allows users to input their medical information and receive a stroke risk prediction. The system uses classification algorithms to analyze the input data and predict the likelihood of stroke. The results show that the system can achieve an accuracy of up to 94.30% in predicting stroke risk. The study also discusses the importance of stroke prediction in healthcare and highlights the potential of machine learning in improving stroke diagnosis and treatment. The system is designed to be cost-effective and efficient, providing personalized warnings and lifestyle advice to reduce stroke risk. Future work includes improving the model's accuracy by using more data and enhancing the system's functionality to provide better user experience. The system is expected to contribute to the development of more effective stroke prevention strategies.