The most common form of dementia, Alzheimer's Disease (AD), is an irreversible neurological disorder that causes progressive mental decline. Although a clear diagnosis of Alzheimer's disease is challenging, in practise, the diagnosis of Alzheimer's disease is mostly dependent on clinical history and neuropsychological data, including magnetic resource imaging (MRI). In recent years, there has been an increase in study on applying machine learning to AD recognition, but this was not efficient for large datasets. This article presents our most recent contribution to the field and also try to propose automatic Alzheimer's disease identification method based on deep learning and 3D brain MRI. In order to achieve more accuracy, we used a Inception V3 Model. This study's CNN is made up of three groups of processing layers, two completely connected layers, and a classification layer. Each of the three groups in the structure is made up of three layers: a convolutional layer, a pooling layer, and a normalization layer. The Alzheimer's Disease Neuroimaging Initiative MRI data was used to train and test the algorithm. The data used included MRI scans of approximately 47 Alzheimer's patients and 34 healthy controls. The experiment demonstrated that the proposed method provided high AD recognition accuracy with a sensitivity of 1 and a specificity of 1.By conducting various experiments on our proposed model, we can able to test MRI image to the system and check the exact condition of the patient