Alzheimer’s disease is a significant public health concern, and early detection is crucial for effective intervention. In this paper, we explore the application of ensemble learning approaches to classify Alzheimer’s disease in brain imaging data (MRI images). We employed several pre-trained deep learning models, including VGG-19, ResNet-152, EfficientNetB1, and EfficientNetB2, to extract valuable features from the imaging data. These models were individually trained for ten epochs, resulting in impressive training and validation accuracies. Specifically, VGG-19 achieved 99.22 and 93.88% accuracy, ResNet-152 achieved 98.64 and 92.71% accuracy, EfficientNetB1 achieved 90.04 and 86.57% accuracy, and EfficientNetB2 achieved 93.28 and 93.29% accuracy. To further improve classification performance, we constructed two ensembles, denoted as Ensemble-1 (VGG-19, ResNet-152, and EfficientNetB1) and Ensemble-2 (VGG-19, ResNet-152, and EfficientNetB2), by combining the individual models. These ensembles exhibited remarkable accuracy, with Ensemble-1 achieving 99.99% training and 95.04% validation accuracy, and Ensemble-2 achieving 99.81% training and 97.16% validation accuracy. The results highlight the potential of ensemble learning in enhancing the accuracy and robustness of Alzheimer’s disease classification, offering a promising avenue for early diagnosis and intervention.