Early identification of individuals at high risk of stroke is essential for improving prognosis, guiding preventive strategies, and reducing the burden of stroke-related disability. This study presents a comparative machine learning approach for stroke risk prediction using structured healthcare data containing demographic, lifestyle, and clinical variables. The proposed approach evaluates five supervised classification models, namely Random Forest, Support Vector Machine, XGBoost, LightGBM, and CatBoost, to identify the most suitable model for stroke risk classification. A systematic preprocessing pipeline was applied, including median imputation for missing BMI values, categorical feature encoding, stratified data splitting, feature scaling, and Synthetic Minority Over-sampling Technique (SMOTE) to address severe class imbalance in the training data. Model performance was assessed using accuracy, precision, recall, and F1-score Among the evaluated models, CastBoost achieved the most balanced overall performance, while SVM showed higher sensitivity in detecting stroke cases. These findings indicate that model selection should depend on the intended clinical purpose, particularly whether the goal is balanced prediction or improved detection of possible stroke cases. Overall, this study highlights the potential of machine learning to support early stroke risk assessment and preventive healthcare decision-making using routinely available structured health data.