Alzheimer’s disease (AD) is one of the most common forms of dementia worldwide. As the condition worsens, it makes it harder for a person to do daily chores independently. The most common illness, Alzheimer’s, begins slowly with modest cognitive impairment and may eventually lead to loss of environmental awareness and communication. For clinical intervention to be successful, AD must be diagnosed accurately and promptly. Even seasoned radiologists may find it difficult to interpret AD from medical imaging. In order to effectively and consistently identify AD traits from Magnetic Resonance Imaging (MRI) images, this study proposes a framework called ViT-BiGRU, which combines a Vision Transformer (ViT) with a Bidirectional Gated Recurrent Unit (BiGRU). Initially, the input MRI is pre-processed using image rescanning, and filtering is done using an Adaptive Gaussian Filter. The Red, Green, and Blue (RGB) Grayscale produces accurate classification results. The pre-processed image is subjected to ViT-BiGRU for classifying output as AD, Cognitively Normal (CN), Early MCI (EMCI), and Late MCI (LMCI). Hyperparameters that tune the ViT-BiGRU model use Guided Remora Optimization (GRO) to increase the training speed. The ADNI dataset is also used in this method. The performance metrics are accuracy, precision, recall, specificity, kappa, f1 score, RMSE, MSE, MAE, and MCC, which attain high performance. The accuracy of the proposed method is 99.45% compared to the existing model, and the error rate is low compared to the existing method. Also, the proposed approach provided insights into disease identification by utilizing Grad-CAM and Grad-CAM++ to provide visual explanations.