Magnetic Resonance Imaging (MRI) has played a vital role in studying the anatomical structure of the brain, enabling the analysis of various neurological conditions and the identification of pathological regions. Alzheimer’s disease, a progressive neurodegenerative disorder, presents a significant public health challenge. Detecting Alzheimer’s disease early and accurately is essential for timely intervention and better patient outcomes. In recent years, deep learning has demonstrated remarkable success in the field of medical image analysis. Through in-depth examinations of tissue architecture enabled by segmented MRI scans, brain diseases can be categorized more precisely. Numerous complex segmentation methods have been introduced for diagnosing Alzheimer’s disease. Given that deep learning algorithms can deliver effective results when working with extensive datasets, they have gained considerable attention for brain structure segmentation and Alzheimer’s disease classification. As a result, deep learning techniques are currently preferred over traditional machine learning methods. This article explores the application of convolutional neural network concepts in studying brain anatomy to detect Alzheimer’s disease. It delves into new techniques, their performance on open datasets, and the advantages of brain MRI segmentation in the categorization of Alzheimer’s disease. The article also briefly reviews the existing literature on Alzheimer’s disease and discusses the potential for deep learning to enhance early diagnosis.