Alzheimer’s Disease (AD) is the most common type of dementia. Alzheimer’s Disease (AD) affects an estimated 1 in 10 people over age 65. Symptoms of Alzheimer’s Disease (AD) usually start slowly and worsen over time. It is an irreversible and progressive brain disorder that slowly destroys memory and thinking skills and eventually the ability to perform simple tasks in daily life. Early detection of Alzheimer’s is extremely important because the treatment could be most effective if the disease can be diagnosed in its early stage. With no known cures, leaving specialists in a race for time on behalf of their patients, there is a pressing need to find biomarkers (short for ”biological markers”) that accurately assess and predict disease progres- sion in symptomless patients. Computer-aided systems to support early diagnosis of the disease provide researchers with a powerful and critical tool for indicating medical intervention at the earliest and most effective stage of the progression of the disease. In this project, Magnetic Resonance Imaging (MRI) brain scan images of Alzheimer’s Disease (AD) patients and control (healthy patients) will investigate. A total of 246 biomarkers were identified, including 123 from the left and 123 from the right brain region. The focus of this project is using various machine learning algorithms with feature selection techniques to classify Magnetic Resonance Imaging (MRI) brain scan images for early Alzheimer’s Disease (AD) detection.