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Cardiovascular diseases (CVDs) represent a leading cause of mortality on a global scale, posing significant challenges to public health and sustainable development (SDG). Both lifestyle behaviors and physiological parameters critically determine an individual's risk of developing cardiovascular conditions. This study utilizes a data-driven methodology to examine the impact of lifestyle factors, including smoking, alcohol consumption, physical activity, and body mass index (BMI), on cardiovascular health. By employing the publicly accessible Cardiovascular Disease Dataset from Kaggle, various machine learning algorithms, such as Logistic Regression, Random Forest, and Support Vector Machine, are implemented to predict cardiovascular risk based on health and behavioral indicators. The findings identify BMI, cholesterol level, and physical activity as the most significant predictors. This research demonstrates the potential of data science techniques in facilitating early risk detection, supporting preventive healthcare strategies, and informing evidence-based interventions aligned with SDG Good Health and Well-Being. By promoting healthier lifestyles and reducing the burden of non-communicable diseases, the study also contributes to SDG Reduced Inequalities through equitable health risk assessment and SDG Industry, Innovation, and Infrastructure by leveraging advanced data analytics for public health improvement.