Timely and adequate identification of Alzheimer’s disease (AD) is essential for its proper treatment. Lately, progress in deep learning and neuroimaging has played considerable roles in facilitating and improving diagnosis. This study represents several attempts and methods based on deep learning techniques for detecting Alzheimer’s disease, such as CNNs, hybrid models, ensemble methods, transfer learning, and complex architecture discrimination. In this setting, CNNs have played a central role in detecting AD by facilitating the examination of spatial hierarchies in MRI images. Hybrid and ensemble models approach the limitations of an individual model by combining many algorithms to increase accuracy and robustness. Using pre-trained models enables transfer learning to improve the diagnosis capability of the disease in situations with scant data. Furthermore, novel technologies such as transformers and multi-stage frameworks complement and enhance existing diagnostic methods. The research proved that by applying such integrated approaches, detection accuracy and their early appearance significantly increased, which illustrates the effect of innovative deep learning technologies on the treatment of Alzheimer’s disease.