The increased incidence of Alzheimer’s disease creates a challenge that demands effective means to produce and apply the most accurate diagnostic methods to detect early and manage effectively. This research work has applied deep learning methodologies from convolutional neural networks, VGG16, VGG19, and ResNet50, to classify the stages of Alzheimer’s disease on a dataset of 2,300 MRI scans. ResNet50 scored the highest by attaining 96.3% accuracy, which is significantly higher than VGG19, with 91.23%, and VGG16, with 86.56%. Precisely, the performance metrics such as precision, recall, and F1 scores indicated that ResNet50 obtained better accuracy in comparison to other models and minimized false positives and negatives, which signifies that it can easily identify subtle changes in the structure of the brain well representative of different stages of the disease. This has proven that deep learning techniques can improve the accuracy of diagnosis in neurology, and the entire stage classification of Alzheimer’s disease will become reliable. In this regard, based on this study, clinicians can apply this knowledge in practice and would be able to successfully insert these models into their routine screening and monitoring of patients with Alzheimer’s. Deep learning can lead to timely and accurate diagnosis, thus enabling patient-specific treatment plans, which may help improve outcomes and possibly arrest the disease’s progression.