Heart disease stands as a primary contributor to global mortality rates, underscoring the critical significance of early detection as a pivotal strategy for effective prevention and treatment. This paper introduces a novel approach that merges feature augmentation techniques with the identification of crucial predictors to boost the accuracy of early heart disease prediction. By expanding the dataset through feature augmentation and selecting the most relevant attributes, our model aims to provide timely and precise risk assessments. Feature augmentation involves the creation of additional data points by leveraging various data transformation methods, including polynomial features and interaction terms. Simultaneously, significant predictors are identified through rigorous statistical analysis and machine learning algorithms. These selected attributes are integrated into predictive models, improving their ability to recognize subtle patterns indicative of heart disease. The study utilizes a comprehensive medical dataset encompassing patient demographics, clinical parameters, and historical health information. Machine learning algorithms, such as logistic regression and random forests, are employed to build predictive models, with model performance assessed using cross-validation and key metrics like accuracy, sensitivity, specificity, and AUC-ROC. Our findings demonstrate that the integration of feature augmentation and significant predictor identification significantly enhances early heart disease prediction accuracy. This approach holds the potential to facilitate timely interventions, ultimately alleviating the burden of heart disease on both individuals and healthcare systems.