This paper addresses the global surge in heart disease prevalence and its impact on public health, emphasizing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is crucial for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases account for 31% of global mortality, with 17.9 million annual fatalities. The intricate clinical landscape, characterized by inherent variability and complex interplay of factors, poses challenges for accurate diagnosis and prediction. Early identification is pivotal for successful treatment. The research presents a comprehensive framework for predicting cardiovascular diseases using advanced boosting techniques and machine learning methodologies, including Catboost, Random Forest, Gradient Boosting, Light GBM, and AdaBoost. The model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library, which includes 8763 samples and 26 feature-based numerical and categorical variables. The empirical findings highlight AdaBoost as the most effective algorithm, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost’s superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction. The paper also discusses the socio-economic impact of heart diseases, the motivations and challenges of the research, and the technical details of the dataset and methods used. The experimental results show that the proposed framework outperforms existing models in terms of accuracy, precision, recall, and F-measure. The feature importance analysis for AdaBoost further enhances the understanding of the most influential features in predicting heart disease. The paper concludes by highlighting the robustness and effectiveness of the proposed framework in early heart disease prediction.This paper addresses the global surge in heart disease prevalence and its impact on public health, emphasizing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is crucial for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases account for 31% of global mortality, with 17.9 million annual fatalities. The intricate clinical landscape, characterized by inherent variability and complex interplay of factors, poses challenges for accurate diagnosis and prediction. Early identification is pivotal for successful treatment. The research presents a comprehensive framework for predicting cardiovascular diseases using advanced boosting techniques and machine learning methodologies, including Catboost, Random Forest, Gradient Boosting, Light GBM, and AdaBoost. The model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library, which includes 8763 samples and 26 feature-based numerical and categorical variables. The empirical findings highlight AdaBoost as the most effective algorithm, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost’s superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction. The paper also discusses the socio-economic impact of heart diseases, the motivations and challenges of the research, and the technical details of the dataset and methods used. The experimental results show that the proposed framework outperforms existing models in terms of accuracy, precision, recall, and F-measure. The feature importance analysis for AdaBoost further enhances the understanding of the most influential features in predicting heart disease. The paper concludes by highlighting the robustness and effectiveness of the proposed framework in early heart disease prediction.