Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that significantly affects cognitive function, making early and accurate diagnosis essential. Traditional Deep Learning (DL)-based approaches often struggle with low-contrast MRI images, class imbalance, and suboptimal feature extraction. This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans. Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient (MCC)-based evaluation method into the design. The trained and validated model gives a 98.88% accuracy rate and 0.9614 MCC score. We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52% ( $\pm 1.51$), a loss of 0.1671, and an MCC score of 0.9429 across folds. The proposed framework outperforms the state-of-the-art models with a 98% weighted F1-score while decreasing misdiagnosis results for every AD stage. The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis. These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis, contributing to improved computer-aided diagnosis (CAD) systems in clinical practice.