Compost addition to polluted soils is a strategy for waste reuse and soil remediation, while bioavailability is a key parameter for environmental assessment. Empirical data from an 8-month microcosm experiment were used to assess the ability and performance of six machine learning (ML) models to predict temporal bioavailability changes of 16 polycyclic aromatic hydrocarbons (PAHs) in contaminated soils amended with compost. The models included multilayer perceptrons (MLPs), radial basis function (RBF), support vector regression (SVR), M5 model tree (M5P), M5 rule (M5R) and linear regression (LR). Overall, the performance of the six models, determined by 10-fold cross validation method, was ranked as follows: RBF > M5P > SVR > MLP > M5R > LR. Results further demonstrated that the ML models successfully identified the relative importance of each variable (i.e. incubation time, organic carbon content, soil moisture content, nutrient levels) on the temporal bioavailability change of individual PAH. Such models can potentially be useful for predicting the concentration of a wide range of pollutants in soils, which could contribute to reduce chemical monitoring at site and help decision making for remediation end points and risk assessment.