Background: Artificial intelligence (AI) is increasingly being integrated into total hip arthroplasty (THA), particularly in preoperative planning, predictive modeling, and postoperative imaging analysis. However, current evidence remains heterogeneous regarding methodology, validation, and clinical applicability. Purpose: To systematically review the current applications, clinical utility, limitations, and future perspectives of AI in THA. Methods: A systematic review was conducted according to PRISMA 2020 guidelines. PubMed/MEDLINE, Scopus, and Web of Science were searched for studies published between January 2015 and May 2026. Original studies evaluating AI-based applications in THA were included. Results: Thirteen studies were included in the qualitative synthesis. The principal AI application domains identified were: (1) preoperative planning and implant templating, (2) prediction of complications and functional outcomes, and (3) implant identification and postoperative imaging analysis. AI-assisted three-dimensional planning systems consistently demonstrated improved implant sizing accuracy and planning reproducibility compared with conventional templating techniques. Machine learning algorithms showed promising performance for prediction of postoperative complications, patient-reported outcomes, and periprosthetic joint infection. Deep learning-based imaging systems also demonstrated high accuracy for automated implant recognition using radiographic imaging. Conclusion: Current evidence suggests that AI-assisted technologies may improve planning accuracy, perioperative risk stratification, and workflow optimization in THA. Nevertheless, the available literature remains predominantly retrospective and heterogeneous, with limited external validation and insufficient prospective clinical evaluation.