Early diagnosis of Alzheimer’s disease is critical for optimizing treatment outcomes and patient care planning. Recent advances in deep learning-based image classification have demonstrated significant potential in reducing misdiagnoses. This study assesses the effectiveness of transfer learning for Alzheimer’s detection utilizing 14 pretrained models. We conducted extensive hyperparameter optimization to improve model generalization on unseen data, aiming to exceed the control set’s test accuracy. Our results reveal that optimized VGG16 achieved superior performance, accurately identifying Alzheimer’s disease from MRI scans with up to 96.72% across two publicly available datasets. These findings highlight the promise of optimized deep learning models in significantly enhancing the precision and reliability of Alzheimer’s disease diagnosis from MRI scans. This improvement could facilitate earlier intervention and ultimately lead to better patient outcomes.