Objective <p>Although numerous population pharmacokinetic (popPK) models of voriconazole have been developed, their predictive performance and extrapolation capacity remain uncertain. This study aimed to systematically evaluate the predictive performance of previously published voriconazole popPK models using an independent external clinical dataset, and to explore potential factors influencing model performance. Additionally, the study sought to identify candidate models with favorable extrapolation capability and clinical utility.</p> Methods <p>The external evaluation dataset was derived from critically ill patients receiving extracorporeal membrane oxygenation (ECMO) support. Published voriconazole popPK models were selected and reconstructed. Predictive performance was assessed using both prediction- and simulation-based diagnostic tools. Furthermore, Bayesian forecasting was applied to evaluate the impact of prior information on the model’s ability to predict individual plasma concentrations.</p> Results <p>A total of 76 voriconazole plasma concentrations from 19 patients were included, and 14 published models were included for evaluation. Diagnostic analyses revealed that none of the models fully satisfied the predefined thresholds for clinical acceptability. Both visual predictive checks (VPC) and normalized prediction distribution errors (NPDE) demonstrated varying degrees of predictive bias across all models, with individual models showing adequate performance only in specific diagnostic aspects. Bayesian forecasting indicated that increasing the number of prior concentrations did not consistently enhance predictive performance. In most models, the use of a single prior concentration was sufficient to markedly improve predictive accuracy and precision, achieving optimal performance. Notably, models incorporating allometric scaling exhibited the greatest improvement in Bayesian predictive performance. Whether the time interval between the prior and predicted concentrations affects predictive performance requires further investigation.</p> Conclusions <p>Published voriconazole popPK models exhibited considerable variability in predictive performance. Multiple factors, including covariates, patient characteristics, and ethnic variability, may serve as critical determinants of model predictive performance and extrapolation capability. Integrating popPK modeling with maximum a posteriori Bayesian (MAPB) estimation and prior concentration information may serve as an effective approach to achieving model-informed precision dosing (MIPD) for voriconazole. Furthermore, incorporating allometric scaling into models may further enhance the performance of Bayesian prediction.</p>