This paper reviews the application of artificial intelligence (AI) in Alzheimer's disease (AD) research, focusing on etiology discovery, computer-aided diagnosis (CAD), computer-aided prognosis (CAP), and treatment. AI technologies, particularly machine learning, have shown significant potential in handling large-scale, high-dimensional, and complex data, which is crucial for understanding AD's etiology and improving diagnostic and prognostic outcomes. The paper highlights the importance of neuroimaging, neuropsychological assessments, and genetic data in AD detection and diagnosis. It also discusses the challenges and future research directions in AD analysis, emphasizing the need for more comprehensive datasets and advanced AI techniques. The review covers various AI applications, including CAD systems that use neuroimaging, linguistic, and genetic data, CAP systems that predict disease progression, and treatment approaches that leverage AI for drug discovery and adverse drug reaction prediction. The paper concludes by discussing the potential of AI in advancing AD research and improving patient care.This paper reviews the application of artificial intelligence (AI) in Alzheimer's disease (AD) research, focusing on etiology discovery, computer-aided diagnosis (CAD), computer-aided prognosis (CAP), and treatment. AI technologies, particularly machine learning, have shown significant potential in handling large-scale, high-dimensional, and complex data, which is crucial for understanding AD's etiology and improving diagnostic and prognostic outcomes. The paper highlights the importance of neuroimaging, neuropsychological assessments, and genetic data in AD detection and diagnosis. It also discusses the challenges and future research directions in AD analysis, emphasizing the need for more comprehensive datasets and advanced AI techniques. The review covers various AI applications, including CAD systems that use neuroimaging, linguistic, and genetic data, CAP systems that predict disease progression, and treatment approaches that leverage AI for drug discovery and adverse drug reaction prediction. The paper concludes by discussing the potential of AI in advancing AD research and improving patient care.