Active pharmaceutical ingredients (APIs) have been repeatedly identified across environmental compartments including freshwater bodies, sediments, and soils, with concerns raised as to their potential toxicity. However, research on the environmental risks of APIs has primarily focused on aquatic environments with less emphasis on terrestrial systems. Exploring the risks of APIs to terrestrial systems requires an understanding of the sorption behaviour of APIs in sewage sludge and soils. This study therefore developed and evaluated new machine-learning based models for estimating the sorption behaviour of APIs in soils and sludges, as well as in aquatic sediments. The models were designed to run using freely available data on the underlying properties of APIs, sludge, soil and sediment parameters that are commonly measured.