<p>Cardiovascular diseases (CVDs) remain one of the leading causes of morbidity and mortality worldwide, necessitating the development of efficient tools for early diagnosis and prevention. In recent years, machine learning (ML) techniques have shown great potential in enhancing the accuracy of CVD prediction models. This paper explores the application of various machine learning algorithms, specifically optimization methods like Quasi-Newton Method, Conjugate Gradient with Powell/Beale Restarts, Fletcher-Powell Conjugate Gradient, Polak-Ribiere Conjugate Gradient Results, and One-Step Secant Method, to predict cardiovascular conditions using large datasets. We focus on evaluating the performance of these algorithms based on key metrics such as accuracy, error rate, recall, specificity, precision, F1-score, Matthews Correlation Coefficient (MCC), and Kappa. Through rigorous model training, data preprocessing, and performance evaluation, our findings demonstrate that these optimization methods can effectively enhance the predictive power of machine learning models for cardiovascular disease diagnosis. Additionally, the paper highlights the importance of fine-tuning and parameter optimization to improve model generalization and minimize overfitting, ultimately contributing to better healthcare outcomes. The results indicate that these machine learning techniques, with appropriate optimization, can significantly improve CVD prediction accuracy, making them valuable tools in modern healthcare for early detection and risk management.</p>