Alzheimer’s disease (AD) poses significant challenges in healthcare due to its progressive nature and the critical need for early diagnosis. Magnetic Resonance Imaging (MRI) is a key modality for identifying structural brain changes indicative of AD. Traditional MRI analysis methods, often manual, suffer from inter-observer variability and inefficiency. While Convolutional Neural Networks (CNNs) have advanced the field of automated image classification, they struggle with capturing long-range dependencies and global context in 3D MRI scans. This study presents the application of Vision Transformers (ViTs) for the classification of 3D MRI images to detect Alzheimer’s disease, with a comparative analysis against CNN models. Leveraging methods for self-attention, ViTs effectively represent long-term dependencies and global context, offering a promising solution to the limitations of CNNs. Our experiments demonstrate that ViTs significantly outperform CNNs in classifying 3D MRI images, achieving superior diagnostic accuracy and facilitating early Alzheimer’s disease detection.