The human heart is the most vital organ in our body that keeps us alive. Heart disease is one of the deadliest diseases which causes many deaths around the globe. In heart disease, the heart fails to supply enough blood to other parts of the body to accomplish its expected functionalities because of the accumulation of fatty plaques in arteries (atherosclerosis). Age, unhealthy lifestyle, family history of heart problems, stress, etc., are a few risk factors for heart disease. The World Health Organization estimates that around 1.79 crore deaths happen worldwide every year. If the heart disease is diagnosed early, and with proper treatment, death rate can be reduced. Machine learning (ML) plays a significant role in detecting heart diseases at an early stage. In this research, experimentations were conducted using various ML methods with the help of a heart disease dataset (HDD) to select a better heart disease prediction system (HDPS). HDD is created by combining publicly available five UCI repository datasets known as Cleveland, Hungary, Switzerland, VA Long Beach, and Statlog heart disease datasets. To improve the efficiency of the heart disease prediction system, experimentation was conducted with ensemble framework referred to as majority voting ensemble framework (MVEF). This ensemble framework was built using EKNN, K-nearest neighbors algorithm (KNN), support vector machine (SVM), a decision tree algorithm, namely, classification and regression tree (CART), and logistic regression (LR) classifiers. MVEF predicted heart disease based on the majority classifiers’ decision. MVEF achieved an accuracy of 94.62%.