Additives and contaminants in polymer‐based medical devices may leach into patients, posing a potential health risk. Physics‐based mass transport models can estimate the leaching kinetics, but they require upper‐bound estimates of solute diffusivity in the polymer. Experiments to measure can be costly and time‐consuming. Alternatives to estimate exist, but they suffer from several drawbacks, such as requiring experimental data to calibrate or specialized knowledge to apply, being limited to certain polymers, or being too time‐consuming given the plethora of polymer/solute combinations in devices. Here, we leverage a large database of diffusivity measurements and apply a machine learning method—quantile random forests (QRF)—to predict bounds on for arbitrary polymer/solute combinations, using only the solute structure and readily available polymer properties (glass transition temperature and density). The most influential factors for determining are these polymer properties and several descriptors related to solute size (e.g., molecular weight ), structure, and interactions. Note that application of the model is limited to the applicability domain defined herein and polymers with relatively low fractional‐free‐volume. We demonstrate the ability of the model to predict and diffusion‐limited transport kinetics, where it compares favorably to other available methods while also overcoming the aforementioned drawbacks.