Preoperative planning, intraoperative navigation, and postoperative rehabilitation of total hip arthroplasty have been significantly enhanced by the integration of Artificial Intelligence (AI) technology. This review summarizes the latest advancements in AI technology for medical image segmentation and registration, with a particular focus on its application in total hip arthroplasty. The notable differences between medical and natural images present challenges for the design of AI algorithms. Deep learning techniques, especially CNN, U-Net, and Transformer models, have demonstrated outstanding performance in various medical image segmentation and registration tasks. AI technology, through deep learning analysis of CT images, has significantly improved the accuracy of identifying hip pathologies. In terms of intraoperative guidance, AI systems provide real-time navigation and precise positioning for surgeries by utilizing intelligent segmentation and motion state simulation, effectively enhancing surgical efficiency. AI technology also encompasses surgical cost prediction and postoperative recovery, offering robust data support for medical decision-making through methods such as Markov models. As deep learning technology continues to advance, the analysis of medical images is progressively achieving automation and intelligence, which has significant clinical implications for improving patients' overall surgical experiences and outcomes, and suggests potential new breakthroughs in the field of medical imaging in the future.