Alzheimer’s disease (AD) is the most common form of dementia and is frequently related to neurological degeneration in the brain. It gradually deteriorates brain cells, leading to memory loss and a downfall in the ability to perform daily activities. Early stage identification of disease is crucial, as it provides an opportunity to predict and manage the challenges patients may face in the future. With advancements in technology, artificial intelligence (AI) is increasingly being used to support the early detection of Alzheimer’s. AI-driven machine learning (ML) algorithms have shown significant potential in accurately identifying and categorizing the disease. In this study, several ML techniques were applied on OASIS dataset to identify and categorize people who are at risk of getting affected by Alzheimer’s in future or are already affected. The performance of various ML-based classifiers were analyzed and examined the classifiers based on their accuracy in detecting AD. Among all the models tested, the AdaBoost classifier performed with e best results, achieving the highest accuracy in identifying Alzheimer’s disease. This highlights the effectiveness of AI-driven solutions in supporting with early diagnosis and improving patient care outcomes.