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Alzheimer disease is heart breaking and Increasing neurological condition that touches the lives of millions around the globe. It leads to memory loss and problems with thinking clearly. Getting diagnosed early is key because it opens the door for timely treatments and interventions. In this paper, we use traditional machine learning methods like Random Forest and Decision Trees. While these methods have done well in diagnosing Alzheimer's, they struggle with spotting complex spatial patterns. That's where our new model comes in. We employed deep-learning methods, especially Convolutional Neural Network and Generative Adversarial Networks, to better classify Alzheimer's disease from MRI scans. Our model achieved a remarkable 93 % accuracy in classification, which is a big step up from traditional methods. What's more, our staging results indicate that deep learning helps the model pick up on complicated features in MRI scans, making it better at detecting changes related to dementia.