This paper provides a comprehensive review of the latest advancements in Deep Learning (DL) techniques and their applications in medical image analysis, particularly for the diagnosis of Alzheimer's Disease (AD). AD is a devastating neurodegenerative disorder that gradually impairs memory and cognitive function, posing a significant global health challenge. The paper aims to elucidate the intricacies of medical image processing and to summarize key findings and recommendations from recent research. The introduction highlights the prevalence and impact of AD, emphasizing the importance of early identification and intervention. It explains the underlying causes of AD, such as the accumulation of amyloid plaques and tau tangles in brain cells, and the progressive loss of nerve cells. The paper also discusses the factors contributing to the increasing incidence of AD, including an aging population, genetic predispositions, and improved diagnostic capabilities. Traditional methods for AD diagnosis, such as clinical evaluation, cognitive tests, and neuroimaging techniques like CT and MRI, are reviewed. While these methods are useful, they are not always conclusive, and post-mortem analysis is sometimes necessary for definitive diagnosis. The paper emphasizes the potential of DL techniques to enhance the accuracy and efficiency of AD diagnosis by leveraging advanced technologies like MRI scans.This paper provides a comprehensive review of the latest advancements in Deep Learning (DL) techniques and their applications in medical image analysis, particularly for the diagnosis of Alzheimer's Disease (AD). AD is a devastating neurodegenerative disorder that gradually impairs memory and cognitive function, posing a significant global health challenge. The paper aims to elucidate the intricacies of medical image processing and to summarize key findings and recommendations from recent research. The introduction highlights the prevalence and impact of AD, emphasizing the importance of early identification and intervention. It explains the underlying causes of AD, such as the accumulation of amyloid plaques and tau tangles in brain cells, and the progressive loss of nerve cells. The paper also discusses the factors contributing to the increasing incidence of AD, including an aging population, genetic predispositions, and improved diagnostic capabilities. Traditional methods for AD diagnosis, such as clinical evaluation, cognitive tests, and neuroimaging techniques like CT and MRI, are reviewed. While these methods are useful, they are not always conclusive, and post-mortem analysis is sometimes necessary for definitive diagnosis. The paper emphasizes the potential of DL techniques to enhance the accuracy and efficiency of AD diagnosis by leveraging advanced technologies like MRI scans.