Alzheimer's disease is a long-term neurodegenerative illness that impacts the brain's cognitive region. Early detection is a key component to proper care management and treatment. This motivated the development of a machine learning model in the detection of AD by working with dataset of MRI scans and demographic information. Hence, this dataset was used in the procedure for training and validation. Early diagnosis of diseases depends on an abundance of various feature extraction techniques and classification algorithms that just help to enhance prediction accuracy. A great potential differentiation between healthy controls and patients with AD is provided in our model, for a potential tool in their early diagnosis. A very important place from the results is highlighted for advanced data driven approaches in medical diagnosis.