Machine Learning-Driven Optimization of Ni-based Catalysts for Catalytic Steam Reforming of Biomass Tar

•Machine learning was used to predict and optimize catalytic toluene reforming.•The reaction temperature has the highest relative importance (0.24).•The optimal reaction temperature for this reaction is 600–700 °C.•The optimal Ni loading and calcination temperature are 5–15 wt% and 500–650 °C.•The optimization was validated through experiments using γ-Al2O3 as a support.