Background <p>Cardiovascular diseases (CVD) are a leading cause of morbidity and mortality globally, highlighting the urgent need for accurate risk prediction to improve early intervention and management. Traditional models have difficulty capturing the complex interactions between risk factors, which limits their predictive power. </p> Objective <p>This paper proposes the OptiStack Classifier, an optimized stacking framework developed to enhance CVD risk prediction through ensemble feature engineering and machine learning techniques. </p> Methods <p>The model uses dimensionality reduction and ensemble feature engineering methods, including polynomial expansion, binning and domain-specific feature transformation, to improve data representation. Principal Component Analysis (PCA) is used to dimensionality reduction, improving computational efficiency. A stacking framework integrates multiple machine learning algorithms as base learners, with Logistic Regression acting as the meta-classifier. Bayesian Optimization is applied for hyperparameter tuning, further boosting predictive performance. </p> Results <p>The proposed model shows significant improvements in predicting CVD risk, helping with early diagnosis and prevention, which can lead to better health outcomes for patients.</p>