Alzheimer’s disease (AD) is a progressive neuro de-generative ailment that decimates the brain memory. The early stage of Alzheimer’s is mild cognitive impairment (MCI) and it is hardly possible to diagnosis. Artificial intelligence (AI) has proliferated in recent years across all scientific disciplines. The early detection of AD is now more accurate and precise thanks to the application of AI in medicine. In the proposed study, introducing a novel technique named ShuffleNet for prognosticating dementia, which is intended to assist doctors in diagnosing AD. Magnetic resonance imaging (MRI) was collected from Alzheimer’s disease Neuroimaging Initiative-3 (ADNI-3) and pre-processed using Histogram Equalization (HE). ShuffleNet a deep neural network combined with the leaky ReLU was used for extracting the surface features from brain MRI. Finally, the proposed system's effectiveness was demonstrated by the correct classification that was acquired using the multi-layered perceptron (MLP) classifier. When compared to CNN's current networks, the suggested model's findings are the best and most accurate. This model yields the sensitivity range of 98.22%, specificity range of 98.75% and accuracy rate of 99.72% respectively with the minimal computational cost.