Giant cell arteritis (GCA), a systemic vasculitis affecting large and medium-sized arteries, poses significant diagnostic and management challenges, particularly in preventing irreversible complications like vision loss. Recent advancements in artificial intelligence (AI) technologies, including machine learning (ML) and deep learning (DL), offer promising solutions to enhance diagnostic accuracy and optimize treatment strategies for GCA. This systematic review, conducted according to the PRISMA 2020 guidelines, synthesizes existing literature on AI applications in GCA care, with a focus on diagnostic accuracy, treatment outcomes, and predictive modeling. A comprehensive search of databases (MEDLINE (via PubMed), Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science) from their inception to September 2024 identified 309 studies, with four meeting inclusion criteria. The review highlights the potential of AI to improve diagnostic accuracy through image analysis of color Doppler ultrasound and clinical data, with AI models like random forests, convolutional neural networks, and logistic regression demonstrating effectiveness in predicting GCA diagnosis and relapse after glucocorticoid tapering. Despite these promising findings, challenges such as the need for larger datasets, prospective validation, and addressing ethical concerns remain. The review underscores the transformative potential of AI in GCA care while emphasizing the need for further research to refine and validate AI-driven tools for broader clinical implementation.