There has been growing interest in using neuroimaging data, such as MRI scans, for the detection of Alzheimer's Disease (AD). Computer vision and deep learning models have shown promise in developing effective Computer-Aided Diagnosis (CAD) models for AD detection and classification. However, many existing models struggle due to their reliance on large training datasets and effective hyper parameter tuning strategies. To address these issues, transfer learning is often used to adjust the final fully connected layers of pre-trained DL models for use with smaller datasets. This paper proposes a new AD classification model based on a combination of Wasserstein GAN-Gradient Penalty (WGANGP) and Deep Transfer Learning (DTL) techniques, aimed at achieving accurate identification and classification of AD on 3D MRI scans. The WGANGP technique is used to increase the size of the dataset, and the model utilizes image enhancement and 3D Spatial Fuzzy C-means (3DS-FCM) techniques for image segmentation. Additionally, feature extraction is performed using the Ant Lion Optimizer (ALO) with the Inception v3 model, while the Deep Belief Network (DBN) model is employed for AD classification. The experimental validation of the WGANGP-DTL model is conducted using a benchmark 3D MRI dataset, and the results show that the proposed model outperforms recent approaches in several aspects.