Background and Objective: Nowadays, with the expansion of human knowledge, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) has found applications in the field of medicine. In the context of infections following hip and knee arthroplasties in orthopedics, machine learning and deep learning have been utilized for assessing the significance of risk factors, evaluating the importance of diagnostic and prognostic criteria, and analyzing the sensitivity of pathogens to antibiotics. Additionally, AI tools can be employed in patient education and in answering common patient questions regarding periprosthetic joint infection (PJI). Materials & Methods: In this narrative study, 22 articles from the past decade, extracted from the PubMed database, were reviewed, focusing on the use of artificial intelligence, machine learning, and deep learning in PJI. We highlighted the aspects analyzed by AI, including risk factors, diagnostic criteria, prognostic factors, and effective antibiotics against bacteria. Furthermore, we recalled common machine learning models along with their respective sensitivity, specificity, and accuracy. We also examined a study regarding the use of ChatGPT-4 for educating and responding to patients with PJI. Results: Given that complex and nonlinear data are effectively and rapidly analyzed by machine learning, significant improvements in the accuracy and speed of PJI management have been achieved through machine learning. Additionally, the use of deep learning has further increased accuracy compared to traditional machine learning methods. Common limitations shared across studies primarily included their retrospective nature and the small sample sizes of the studied populations. Conclusion: Considering the capabilities of AI, machine learning and deep learning to analyze data more quickly and accurately compared to traditional methods, utilizing these technologies in various aspects of medicine, including PJI management, is reasonable.