Biochar is an effective material for the removal of heavy metals from wastewater. Operational conditions, such as metal initial concentration, temperature, contact time as well as the presence of competing ions can impact the effectiveness of the treatment process. While several models have been proposed for modelling the adsorption process, no model currently exists that accounts for the mutual interactions of key process parameters on the adsorption capacity in multi-solute systems. The aim of this study is to address this gap in knowledge by formulating a multi-input multi-output (MIMO) model, which takes into account the effect of mutual interactions of key factors while predicting heavy metals adsorption capacity of the biochar in single and multi-solute systems. In this study, we use machine learning models, specifically several ANN models, radial basis and gradient boosting algorithms to model the MIMO process. The results of our models provide highly accurate predictions (R2 > 0.99). The generalized regression network provided the best match to the experimental data. This approach can allow operators to predict how the adsorption system will respond to changes in the operations and hence provide them with a tool for process optimization.