Alzheimer’s disease, though not curable, can be effectively managed, particularly when identified early. Timely intervention significantly influences its progression, enhancing the patient’s well-being and relieving caregiver burden. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset offers valuable information for machine learning analysis. However, incomplete data is a common challenge due to the high cost of tests like electroencephalograms and magnetic resonance imaging. Our proposal involves employing machine learning techniques, including ensemble methods and ADA Boost, to analyze the ADNIMERGE dataset, which includes neuropsychological assessment scores. These algorithms will address incomplete data and balance the dataset by generating synthetic observations, aiming to enhance model performance in detecting Alzheimer’s disease stages. The ensemble approach combines multiple models to improve predictive accuracy, while ADA Boost trains weak learners iteratively to enhance overall model precision. Our goal is to develop a cost-effective tool for Alzheimer’s disease detection using only neuropsychological assessments.