Introduction Perfluoroalkyl acids (PFAAs) are a subgroup of per- and polyfluoroalkyl substances (PFAS) that are extremely persistent and linked to adverse health effects in humans and animals. The European Union has set maximum levels for four PFAAs in foods of animal origin to limit dietary exposure. Predicting PFAA accumulation kinetics in livestock is therefore crucial for assessing their transfer into food products. This study aims to develop a physiologically based toxicokinetic (PBTK) model for branched (br-) and linear (n-) PFAAs in dairy goats. The model covers several perfluorocarboxylates and perfluorosulfonates with chain lengths from 4 to 13 carbons and quantifies their distribution across tissues and excretory routes. Materials and methods The dynamic compartmental model represents twelve physiological compartments (blood plasma, liver, kidney, spleen, stomachs, intestines, mammary gland, heart, brain, muscles, lungs, and rest) and three excretion pathways (urine, feces, milk). It was parameterized using literature data and results from an in-house in vivo feeding study, in which eight goats received PFAS-contaminated hay for eight weeks followed by a twelve-week depuration phase. PFAA concentrations were measured in serum, milk, and excreta over time, and in body tissues at slaughter (end of depuration). Key parameters were optimized to minimize prediction error, and model uncertainty was quantified using jackknife and Monte Carlo analyses. The model, implemented in Python (v3.14), applies an analytical matrix solution enabling efficient computation. Results and discussion Preliminary results show that the model achieved high predictive performance across 17 PFAAs (R² = 0.96; mean absolute error [MAE] = 0.18) with 86% confidence interval coverage, indicating robust uncertainty characterization. Predicted mean residence times ranged from 23.3 h (half-life = 0.7 days) for branched perfluorotridecanesulfonic acid (br-PFTrDS) to 427.9 h (half-life = 12.4 days) for non-branched perfluorooctanesulfonic acid (n-PFOS), reflecting the influence of molecular structure on the kinetics of different PFAAs. Conclusion and implications This model enables the simulation of accumulation and depuration under various exposure scenarios. It may support quantitative risk assessment by predicting transfer into edible tissues and milk and can address questions related to animal health by estimating internal exposure in livestock.