Recently, machine learning (ML) models have made significant progress in predicting postoperative outcomes after joint replacement. Particularly in predicting postoperative functional recovery and complications, their accuracy far surpasses that of traditional statistical methods. ML models can integrate diverse data sources (such as demographic, clinical, and imaging data) and capture complex nonlinear relationships, thereby providing more precise individualized risk predictions. However, the clinical application of ML models still faces multiple challenges, including difficulties in data integration due to the lack of standardized medical data, trust issues arising from the "black box" nature of high-performance models, as well as constraints related to model generalization, legal regulations and compliance, and data security. This review aimed to explore the research progress of ML models in predicting postoperative outcomes after joint replacement.