Dementia is a neurodegenerative disease that causes a progressive deterioration of thinking, memory, and the ability to perform daily tasks. Other common symptoms include emotional disorders, language disorders, and reduced mobility; however, self-consciousness is unaffected. Dementia is irreversible, and medicine can only slow but not stop the degeneration. However, if dementia could be predicted, its onset may be preventable. Thus, this study proposes a revolutionary transfer-learning machine-learning model to predict dementia from magnetic resonance imaging data. In training, k-fold cross-validation and various parameter optimization algorithms were used to increase prediction accuracy. Synthetic minority oversampling was used for data augmentation. The final model achieved an accuracy of 90.7%, superior to that of competing methods on the same data set. This study's model facilitates the early diagnosis of dementia, which is key to arresting neurological deterioration from the disease, and is useful for underserved regions where many do not have access to a human physician. In the future, the proposed system can be used to plan rehabilitation therapy programs for patients.