Heart disease is a major worldwide health problem, and successful treatment and better patient outcomes depend on early and correct diagnosis. The use of machine learning techniques to help doctors diagnose heart problems has showed promise. The ensemble strategy suggested in this study combines many machine learning models to improve the precision of heart disease detection. The ensemble technique combines different machine learning algorithms, including Gradient Boosting, Support Vector Machines (SVM), and Random Forest, into a single framework. To capture a complete depiction of the illness patterns, each model is trained on a broad set of data obtained from medical records, clinical measures, and patient history. Using a sizable dataset of anonymized patient records with a verified diagnosis of heart disease, the effectiveness of the ensemble technique is tested. The collection includes data on demographic traits, symptoms, lab test outcomes, and reports from medical imaging. The ensemble model's diagnostic adequacy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) are then assessed against individual machine learning models and conventional diagnostic techniques