In Alzheimer's disease (AD), memory and cognitive abilities deteriorate, ultimately affecting the capacity to do basic activities. In and around brain cells, aberrant amyloid and tau protein accumulation is believed to cause it. Amyloid deposits create plaques surrounding brain cells, whereas tau deposits form tangles inside brain cells. The plagues and tangles harm healthy brain cells, causing shrinkage. This damage seems to be occurring in the hippocampus, a brain region involved in memory formation. There are presently no methods that provide the most accurate outcomes. The current techniques do not identify AD early. The proposed DL-EPAD method achieves excellent clustering quality using MGKFCM (Modified Gaussian Kernel Fuzzy C-means Clustering) method. The MGKFCM utilizes an Elbow Method to determine the number of clusters in a dataset. Unlike other medical pictures, brain scans are extremely sensitive. The pictures should be visible, and the noise should be minimal. The study utilizes Deep Learning, which outperforms other conventional machine learning techniques. Convolutional Neural Networks (CNN) utilizes neuroimaging data without pre-processing to pick AD classification features. The suggested approach outperforms current techniques (98%).