Azole fungicides are highly suspected endocrine disruptors (EDs) and are frequently detected in surface water. Among them, there are prochloraz (PCZ), a commonly used molecule for ED studies, and imazalil (IMZ), a highly suspected ED. Little is known about their toxicokinetic (TK) behavior in fish. Hence, research suggested that an improved risk assessment could be achieved by gaining insight into their TK behavior. The aim of this study is to understand and model the TK of both substances in different fish species, irrespective of the scheme of exposure. TK data from the literature were retrieved including different modes of exposure (per os and waterborne). In addition, two experiments on zebrafish exposed to either IMZ or PCZ were performed to address the lack of in vivo TK data. A physiologically based kinetic (PBK) model applied to IMZ and PCZ was developed, capable of modeling different exposure scenarios. The parameters of the PBK model were simultaneously calibrated on datasets reporting internal concentration in several organs in three fish species (original and literature datasets) by Bayesian methods (Monte Carlo Markov Chain). Model predictions were then compared to other experimental data (i.e., excluded from the calibration step) to assess the predictive performance of the model. The results strongly suggest that PCZ and IMZ are actively transported across the gills, resulting in a small fraction being effectively absorbed by the fish. The model’s results also confirm that both molecules are extensively metabolized by the liver into mainly glucuronate conjugates. Overall, the model performances were satisfying, predicting internal concentrations in several key organs. On average, 90% of experimental data were predicted within a two-fold range. The PBK model allows the understanding of IMZ and PCZ kinetics profiles by accurately predicting internal concentrations in three different fish species regardless of the exposure scenario. This enables a proper understanding of the mechanism of action of EDs at the molecular initiating event (MIE) by predicting bioaccumulation in target organs, thus linking this MIE to a possible adverse outcome.< Réduire