Alzheimer’s disease (AD) is a major medical challenge due to its persistence and irreversibility and lack of a cure. The illness has no cure, although contemporary pharmaceutical therapies have halted its progress. Early AD diagnosis slows disease progression. The research categorizes medical photos to follow Alzheimer’s disease and identify it rapidly. Deep learning, specifically Convolutional Neural Networks, analyzes and understands such jobs. The spectrum may be separated into four phases to understand AD’s evolution. Each pair of AD stages uses a unique binary classification technique to improve diagnosis and accuracy. Two methods accurately diagnose Alzheimer’s and categorize medical photos. First, a CNN architecture detects 2D and 3D structural brain pictures from the Alzheimer’s Disease Neuroimaging Initiative dataset. The second method classifies medical images using transfer learning and pre-trained models like VGG19. Through empirical analysis of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, this study compares deep learning models including SVM, Decision Trees, Random Forests, and k-NN versus shallow models. Deep learning outperforms superficial methods. This technique indicates that multimodal data integration enhances accuracy, precision, recall, and mean F1 scores over single-modality models. This validates multi-author Alzheimer’s diagnosis and treatment.