Goal: This work focuses on neuroimaging studies, including MRI analysis via machine learning and deep learning, which has led to an increase in computer vision research and the early detection of neural disorders. Methods: An adaptive implementation of transfer learning (TL) with a top-level VGG16 architecture is set up with pretrained weights for large MRI images dataset. Convolutional neural networks (CNN) are a bespoke version of a multi-layer view. Through experimentation on the ADNI dataset, the algorithm was trained and tested in binary and multiclass classification using the MRI scanning of individuals. Results: The machine was trained on the CNN model of binary classification and the CNN model for multiclass classification and was trained using TL (using the VGG16 model) with CNN; 25 epochs of batch size of 32 were considered. To validate the contributions of this study, we demonstrated that the proposed model used for binary classification gave an accuracy of 96.89% and an F1 score of 96.90%, and for multiclass classification, we obtained an accuracy of 99.89% and an F1 score of 94.82%. Conclusion: The suggested approach is highly generic, as it is simple and parameter invariant, and therefore applicable to any MRI dataset.