Alzheimer’s disease (AD) poses a growing global health concern, demanding innovative approaches for accurate diagnosis and early prediction. investigates the potential of machine learning algorithms in predicting the early onset of AD. Through the analysis of a comprehensive dataset containing biomarkers and clinical information, we explore various machine-learning models to identify individuals at risk of developing AD in its initial stages. This research underscores the significance of leveraging computational techniques to provide early warnings for timely interventions and personalized patient care. We focus on enhancing the accuracy of AD classification through ensemble techniques. By aggregating predictions from multiple machine learning models, we introduce a voting ensemble method to improve the overall classification performance of AD cases. This approach demonstrates the potential of combining diverse models to mitigate individual weaknesses and achieve more reliable diagnostic outcomes, contributing to refined clinical decision-making processes.