Alzheimer’s is a degenerative brain cell disease that affects around 5.8 million people globally. The progressive neurodegenerative disease known as Alzheimer’s Disease (AD), affects the frontal cortex, the part of the brain in charge of memory, language, and cognition. As a result, researchers are utilizing a variety of machine-learning techniques to create an automated method for AD detection. The massive data collected during ROI and biomarker identification takes longer to handle using current methods. This study uses metaheuristic-tuned deep learning to detect the AD-affected region. The research utilizes advanced deep learning and image processing techniques to enhance early and accurate diagnosis of Alzheimer’s disease, potentially enhancing patient outcomes and prompt therapy. The capacity of deep neural networks to extract complex patterns from magnetic resonance imaging (MRI) scans makes them indispensable in the diagnosis of AD since they allow the detection of minor aberrations and complex alterations in brain structure and composition. An adaptive histogram approach processes the collected photographs, and a weighted median filter is used in place of the noisy pixels. The next step is to identify the issue region using a deep convolution network-based clustering segmentation process. A correlated information theory approach is used to extract various textural and statistical features from the separated regions. Lastly, the selected features are probed by the fly-optimized densely linked convolution neural networks. The method surpasses state-of-the-art techniques in sensitivity (15.52%), specificity (15.62%), accuracy (9.01%), error rate (11.29%), and F-measure (10.52%) for recognizing AD-impacted regions in MRI scans using the Kaggle dataset.