Alzheimer’s Disease (AD) is the major cause of dementia among older persons. Currently, there is a lot of interest in using Machine Learning to identify critical disorders that impact a huge number of people worldwide, such as Alzheimer’s. Each year, their incidence rates rise at a startling rate. Neurodegenerative alterations impact the brain in Alzheimer’s Disease. An increasing number of people, their families, and healthcare professionals will suffer from illnesses that impair memory and functioning as our population ages. These impacts will have a significant impact on the financial, social, and economic fronts administered later. For those who are impacted, early identification of Alzheimer’s Disease offers significant benefits. Machine Learning provides a remedy by enabling early disease diagnosis and prediction. This study’s main goal is to detect dementia in patients by using a variety of Machine Learning (ML) methods. Despite its tiny size, the Open Access Series of Imaging Studies (OASIS) dataset contains useful information for creating diagnostic models. The study included six Machine Learning techniques, including (SVM, Logistic Regression, KNN, Decision Tree, Naive Bayes, Random Forest) and focused on the four models with the best accuracy. The best result 98.52% was achieved with logistic regression. Metrics like Precision, Recall, Accuracy, and F1-score for ML models are applied to gauge performance.