Alzheimer’s disease (AD) is a progressive and irreversible neurological disorder that affects millions of people worldwide. Early detection and accurate diagnosis of AD are crucial for effective treatment and management of the disease. In this paper, we propose a transfer learning-based approach for the diagnosis of AD using magnetic resonance imaging (MRI) data. Our approach involves extracting relevant features from the MRI data using transfer learning by alter the weights and then using these features to train pre-trained models and combined ensemble classifier. We evaluated our approach on a dataset of MRI scans from patients with AD and healthy controls, achieving an accuracy of 95% for combined ensemble models. Our results demonstrate the potential of transfer learning-based approaches for the early and accurate diagnosis of AD, which could lead to improved patient outcomes and more effective management of the disease.