Prediction of pharmacokinetic (PK) properties is essential for early drug candidate screening and dosage regimen optimization. In recent years, using machine learning/deep learning approaches for predicting pharmacokinetic properties directly from chemical structures has attracted increasing attention. In this study, we propose multifidelity pharmacokinetic learning (MFPK), a transfer-learning framework for predicting intravenous pharmacokinetic parameters across multiple species, including humans, dogs, monkeys, rats, and mice. MFPK incorporates graph-, motif-, and three-dimensional structure-based molecular representations to capture comprehensive, multiscale chemical information. Comparative evaluations demonstrate that MFPK outperforms baseline models across multiple tasks, particularly volume of distribution at steady state (VDss) across all species (root-mean-square of logarithmic error (RMSLE) < 0.48, geometric mean fold error (GMFE) < 2.3). Furthermore, interpretability analyses were conducted to provide insights into model decision-making and mitigate the black-box nature of deep learning models. The MFPK model is accessible at https://lmmd.ecust.edu.cn/MFPK.