The (AD) is a progressive neurodegenerative condition that has a critical impact on cognitive functions. Thus, timely detection is needed to allow timely intervention and enhance the success of treatment. Over the last few years, a hybrid of big data and deep learning has become one of the effective approaches to the improvement of the accuracy of diagnosis. Convolutional Neural Networks (CNN) and an optimized hybrid method that combines CNN with the Harris Hawks Optimization (HHO) algorithm are comparatively analyzed in this chapter to predict the Alzheimer’s disease. The CNN model showed a high performance of 88.7% on large-scale medical imaging datasets, showing that it can automatically detect the complex features related to AD detection. Nevertheless, as HHO was added to optimize the hyperparameters and select the features, the CNN + HHO model achieved great performance, and its accuracy was 97.2%. These results underscore the possibility of hybrid deep learning models to provide strong and consistent early detection of Alzheimer’s disease. The chapter goes to talk about the clinical implications of these findings, too and the importance of optimization based deep learning in improving precision healthcare.