Cardiovascular disease (heart disease) is a silent killer worldwide. These days, automated diagnosis is valuable for precise and quick solutions. This research uses a unique application-oriented model to identify cardiac disease. Cardiovascular disease (CVD) is the term related to the heart (cardio) and the blood vessels (vascular). For disease prediction, the suggested method incorporated 13 medical parameters. The results are compared to SVM, Gaussian Naive Bayes, Logistic Regression, and Ensemble model (EMRFLM), combining two algorithms, Logistic Regression, and Random Forest, to make a novel algorithm and XGBoost algorithms. The proposed deep learning algorithm extreme learning machine algorithm fetches us better accuracy, helps better prediction of heart condition, and tends to outperform attaining an accuracy of 94.5%.