This paper presents a comprehensive framework for predicting cardiovascular diseases using boosting ensemble techniques, including AdaBoost, CatBoost, Gradient Boosting, Light GBM, and Random Forest. The study addresses the global surge in heart disease prevalence and its impact on public health, emphasizing the need for accurate predictive models. The World Health Organization (WHO) reports that cardiovascular diseases account for 31% of global mortality, with 17.9 million annual fatalities. The research uses a dataset from the UCI machine learning library containing 8763 samples with 26 features, including age, gender, cholesterol levels, blood pressure, and lifestyle factors. The empirical findings highlight AdaBoost as the most effective model, achieving 95% accuracy with high precision, recall, and low false positive/negative rates. The study also compares various machine learning techniques, including deep learning and ensemble learning, to evaluate their performance in predicting heart disease. The results show that AdaBoost outperforms other algorithms in terms of predictive accuracy and overall performance. The research contributes to the development of robust models for cardiovascular health prediction, emphasizing the importance of accurate diagnosis and early intervention. The study also discusses the socioeconomic impact of heart diseases, highlighting the need for effective predictive models to reduce the global burden of cardiovascular diseases. The proposed framework leverages advanced machine learning techniques to improve the accuracy and reliability of heart disease prediction, providing valuable insights for healthcare professionals and policymakers.This paper presents a comprehensive framework for predicting cardiovascular diseases using boosting ensemble techniques, including AdaBoost, CatBoost, Gradient Boosting, Light GBM, and Random Forest. The study addresses the global surge in heart disease prevalence and its impact on public health, emphasizing the need for accurate predictive models. The World Health Organization (WHO) reports that cardiovascular diseases account for 31% of global mortality, with 17.9 million annual fatalities. The research uses a dataset from the UCI machine learning library containing 8763 samples with 26 features, including age, gender, cholesterol levels, blood pressure, and lifestyle factors. The empirical findings highlight AdaBoost as the most effective model, achieving 95% accuracy with high precision, recall, and low false positive/negative rates. The study also compares various machine learning techniques, including deep learning and ensemble learning, to evaluate their performance in predicting heart disease. The results show that AdaBoost outperforms other algorithms in terms of predictive accuracy and overall performance. The research contributes to the development of robust models for cardiovascular health prediction, emphasizing the importance of accurate diagnosis and early intervention. The study also discusses the socioeconomic impact of heart diseases, highlighting the need for effective predictive models to reduce the global burden of cardiovascular diseases. The proposed framework leverages advanced machine learning techniques to improve the accuracy and reliability of heart disease prediction, providing valuable insights for healthcare professionals and policymakers.