Alzheimer’s disease (AD) is a major public health issue that mainly affects older adults, resulting in progressive memory loss and various cognitive difficulties over time. Recent studies emphasize the necessity of diagnosing Alzheimer’s disease at an early stage. This disorder causes a steady reduction in cognitive processes, resulting in mental degeneration. In recent years, there has been a significant increase in programs targeted at detecting and preventing the progression of Alzheimer’s. According to research, genetics, stress, and diet all have significant factors in the development of this illness. Our study combines Convolutional Neural Networks (CNN) with Vision Transformer to diagnose Alzheimer’s disease using deep learning approaches. The CNN analyzes characteristics in brain images, allowing the system to differentiate between normal and AD-affected ones. We assessed the efficacy of our approach using the OASIS dataset (Open Access Series of Imaging Studies), providing valuable insights into its potential for early AD detection.The conclusion of our study outlines potential opportunities for future research and offers suggestions for upcoming investigations into the diagnosis of Alzheimer’s disease.