The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN) and random forest (RF) based on 353 dataset of adsorption experiments from literatures. The regression models were trained and optimized to predict the adsorption capacity according to biochar characteristics, metal sources, environmental conditions (e.g. temperature and pH), and the initial concentration ratio of metals to biochars. The RF model showed better accuracy and predictive performance for adsorption efficiency (R = 0.973) than ANN model (R = 0.948). The biochar characteristics were most significant for adsorption efficiency, in which the contribution of cation exchange capacity (CEC) and pH of biochars accounted for 66% in the biochar characteristics. However, surface area of the biochars provided only 2% of adsorption efficiency. Meanwhile, the models developed by RF had better generalization ability than ANN model. The accurate predicted ability of developed models could significantly reduce experiment workload such as predicting the removal efficiency of biochars for target metal according to biochar characteristics, so as to select more efficient biochar without increasing experimental times. The relative importance of variables could provide a right direction for better treatments of heavy metals in the real water and wastewater.