The number of people affected by Alzheimer’s disease is increasing year by year. It is a progressive neurodegenerative disorder that impairs memory and cognitive functions. Despite extensive research, there is currently no cure, although existing therapies slow its progression. This study focuses on utilizing artificial intelligence methods to precisely and rapidly detect Alzheimer’s disease. We propose a novel method for Alzheimer’s detection based on standardized MRI preprocessing and transfer learning using the VGG16 architecture. The proposed approach outperforms existing solutions due to its robust data selection strategy, which prevents data leakage, its computational efficiency, and its ability to balance precision and sensitivity. Experimental studies have shown that the method achieves nearly 90% accuracy on the test dataset. These promising results demonstrate that the method can effectively support doctors in analyzing MRI images to diagnose Alzheimer’s disease.