Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early diagnosis and classification of AD are crucial for effective treatment and care. Here we explore the application of convolutional neural networks (CNNs) and the VGG16 architecture on the prediction and classification of Alzheimer’s disease. Our study demonstrates remarkable accuracy rates, with CNN achieving an accuracy of 97% and VGG16 achieving an even higher accuracy of 98%. These results were obtained through the analysis of medical imaging data, specifically magnetic resonance images (MRIs), which contain valuable information about brain structural changes associated with AD. The proposed models offer promising capabilities for automated AD diagnosis, which can greatly aid healthcare professionals in early intervention and personalized patient care. This proposed work showcases the potential of deep learning techniques in addressing critical healthcare challenges and highlights the significance of advanced image analysis in the field of neurodegenerative disease diagnosis.