Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that deteriorates memory and cognitive functions across multiple stages. Timely and accurate classification of these stages—Normal Control (NC), Mild Cognitive Impairment (MCI), Moderate AD, and Severe AD—can facilitate better treatment planning. This research investigates the efficacy of the ResNet50 deep residual neural network in multi-stage classification of AD using Magnetic Resonance Imaging (MRI) data. A dataset curated from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) comprising 7,200 MRI scans was utilized. Preprocessing included intensity normalization, skull stripping, and augmentation to mitigate class imbalance. ResNet50, leveraging skip connections, was trained with categorical cross-entropy and Adam optimizer. Performance metrics including Accuracy (92.1%), Precision (90.3%), Recall (91.2%), and F1-score (90.7%) demonstrate ResNet50’s capability in capturing discriminative patterns across AD stages. The results suggest its potential as a reliable computer-aided diagnosis (CAD) tool in clinical settings.