The detection of early morphological changes in the brain and early diagnosis of Alzheimer’s disease (AD) are important in the field of healthcare. It is possible to use high-resolution magnetic resonance imaging (MRI) to help diagnose and predict this disease. The objective of this chapter is to study, analyze, and tackle the recent published segmentation, feature extraction, feature selection, optimization, and classification algorithms and their state-of-the-art for the diagnosis of AD. Some of these algorithms are based on histogram-based segmentation, region-based segmentation, edge-based segmentation, clustering segmentation, gray level co-occurrence matrix, linear discriminant analysis, deep learning, deep neural network, support vector machine, principal component analysis, and genetic algorithm. These algorithms and tools act as a way of understanding and studying the different relationships and associations of patterns hidden in the image data. Furthermore, this research presents the strengths and weaknesses of each algorithm to explore the best top algorithms used in diagnosing AD.