Hepatocellular carcinoma (HCC) is the most common primary liver cancer and ranks among the most prevalent and highly lethal cancers worldwide. Radical hepatectomy remains one of the most effective treatment methods for early and some intermediate to advanced stages of HCC. Complications during the perioperative period of liver resection are critical factors affecting the long-term and short-term prognosis of HCC patients, with post-hepatectomy liver failure (PHLF) being a common complication after liver resection. PHLF is a major cause of perioperative death in liver resection patients, and the timely identification of patients with high-risk PHLF before surgery is a pressing clinical issue and research focus. Traditional methods of liver function assessment are widely used and can distinguish high-risk PHLF patients, but their predictive accuracy is relatively low. In recent years, with the development of artificial intelligence technology, an increasing number of advanced algorithms and models incorporating more comprehensive risk factors have been applied in the field of PHLF prediction. Scholars both in China and abroad have constructed new PHLF-related prediction models through various statistical methods, confirming a significant improvement in the accuracy of these models. After an extensive literature review, the authors summarize the relevant literature on PHLF risk prediction models, providing a comprehensive overview of the research progress, so as to facilitate clinicians and researchers in gaining a more comprehensive understanding of various types of PHLF prediction models.