Alzheimer's disease (AD) is a neurodegenerative disorder that severely impacts cognitive function, posing significant physical and psychological burdens on patients and substantial economic challenges to families and society, particularly in aging populations where its prevalence is rising. Current diagnostic and therapeutic strategies, including pharmacological treatments and non-pharmacological interventions, exhibit considerable limitations in early diagnosis, etiological treatment, and disease management. This study aims to investigate the application of artificial intelligence (AI) in the early diagnosis and progression monitoring of AD through a bibliometric analysis of relevant literature. A systematic search in the Web of Science Core Collection identified 530 publications related to AI and AD, consisting of 361 original research articles and 169 review articles, with a notable increase in annual publication rates, particularly between 2019 and 2024. The United States and China emerged as leading contributors, emphasizing the importance of international collaboration. Institutional analysis revealed that Harvard University and Indiana University System are at the forefront, highlighting the role of academic institutions in fostering interdisciplinary research. Furthermore, the Journal of Alzheimer's Disease was identified as the most influential publication outlet. Key highly cited papers provided essential theoretical foundations for ongoing research. This study underscores the growing relevance of AI in AD research and suggests promising avenues for future investigations, particularly in enhancing diagnostic accuracy and therapeutic strategies through advanced data analytics and machine learning techniques.