This literature review focuses on the development and evaluation of data-driven predictive models for periprosthetic joint infection (PJI) using extensive electronic health record (EHR) data. Researchers used data from an institutional arthroplasty registry which included 58,574 procedures performed on 41,844 patients. Various machine learning and regression methodologies, including lasso regression, ridge regression, random forests, and neural networks, were tested to predict PJI. Model accuracy was assessed through 10-fold cross-validation, focusing on discrimination (c-statistic) and calibration. Results showed strong predictive capabilities across models, with relaxed lasso regression exhibiting the best performance, achieving concordances of 0.681–0.850 depending on surgery type, utilizing between nine and 41 predictors.