Predictive analytics plays a vital role in the healthcare industry for providing timely insights and decisions to all the stakeholders of the healthcare industry. Machine Learning has become predominant in the healthcare industry for predicting various diseases that cause severe health issues in humans. One such disease is Alzheimer’s, a common neurological condition that affects a sizable number of the elderly population. Although the symptoms of this disease are very minimal in the beginning, they become critical over time and can lead to dementia. Early prediction of this disease can improve a patient’s health status and facilitate the improvement of targeted interventions. This study uses various machine learning algorithms such as Logistic Regression, KNN, Decision Tree, SVC, Random Forest, Hard Voting Classifier, Soft Voting Classifier, Gradient Boosting Classifier, Extreme Gradient Boosting Classifier, and Ensemble Learning Model to predict Alzheimer’s disease. This research uses the Open Access Series of Imaging Studies (OASIS) longitudinal dataset to build different machine learning models by using multiple machine learning algorithms to predict Alzheimer’s disease. The obtained results clearly indicate that ensemble model achieved a higher validation accuracy of 87.3% and test accuracy of 85.3%, in comparison with the other machine learning algorithms used in this work.