The Alzheimer's disease is a progressive neurological disorder that gradually impairs cognitive abilities and behavior, affecting daily functioning. Current treatments for this medical condition are insufficient in addressing its unpredictability. Due to the intricate structure of the brain, it is extremely challenging to identify Alzheimer's disease beyond its early stages. Deep learning-based computer-aided diagnosis (CAD) methods utilizing MRI imaging data have been proposed as accurate tools for identifying Alzheimer's disease during its initial phases. In this study assessed the effectiveness of deep learning techniques on magnetic resonance imaging (MRI) scans in diagnosing Alzheimer's disease. Furthermore, this paper offers an extensive overview of related studies comparing Alzheimer's disease (AD) patients versus cognitively normal (CN) individuals. Although deep learning displays promise in diagnosing AD, several concerns persist. Deep learning models hold great significance for managing Alzheimer's disease. Developing a thorough diagnostic framework encompassing all forms of dementia—not only Alzheimer's disease—is essential to advance clinical practices and improve patient outcomes in the long run.