This study evaluates the performance of a convolutional neural network (CNN) model for Alzheimer’s disease (AD) classification based on MRI image processing. The results show that after 22 epochs of training, the model achieved a validation accuracy of 80.61%. Furthermore, the model exhibited high precision (78.99%) and recall (30.55%) rates, along with a significant area under the curve (AUC) value of 86.05%. These findings suggest the potential of the CNN model in accurately identifying AD cases using MRI scans, emphasizing its effectiveness as a diagnostic tool for early detection and intervention in AD.