Heart disease has been on the rise in recent decades, and there are several potential explanations. Prompt identification of cardiac disease is crucial in the medical industry. This study implemented a method for early prediction of heart disease using machine learning. Healthcare providers commonly use ML algorithms to predict potentially catastrophic illnesses. Enhancing model accuracy is achieved through the application of six classification models: logistic regression, K-nearest neighbour, random forest, support vector machine, decision tree, and XGBoost. These models incorporate feature standardization, hyperparameter tweaking through GridSearchCV, and correlation-based feature selection (CFS). The objective of the model is to predict cardiac conditions using hybrid ensemble techniques with an enhanced CFS. We improved the accuracy of our cardiac disease model by standardizing the dataset and fine-tuning its hyperparameters using the dataset. To further improve the accuracy of the proposed system, standardization and correlation-based feature selection were utilized. We used K-fold cross-validation to teach and validate our models. To fine-tune the hyperparameters of the six classification techniques, GridSearchCV was employed. Compared to traditional models, the proposed method outperforms them when it comes to forecasting cardiac disease using a combination of feature selection plus hyperparameter tweaking. To evaluate the efficacy of conventional classifiers versus those enhanced through feature selection as well as hyperparameter tuning, we utilized recall, accuracy, precision, and F-score. Using a mixed ensemble approach that included feature selection along with hyperparameter tuning yielded the best results in terms of accuracy.