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Hydrogen energy is widely regarded as one of the most promising clean energy sources for carbon emissions reduction due to its high-grade energy and complete clean combustion. Since the mechanism of the production process is difficult to elucidate and efficient production cannot be maintained at all times, it has the disadvantages of instability and low efficiency of production processes. The forecasting results can help the biomass based hydrogen production process to adjust the material input in time, thus ensuring a stable and efficient production. Therefore, to solve those problems, the dynamic hydrogen production yield forecasting model based on the Discrete Grey Method (DGM) and the Gradient Boosting Regression Tree (GBRT) has been proposed. The results show that the forecasting results of the GBRT based model have a strong correlation with the input data, while the DGM based model can eliminate some unreasonable forecasting results. Thus, the proposed model can get good forecasting results in different scenarios since its MAE is 0.2. To verify the proposed model, five widely used forecasting models are used as the contrasting models. The forecasting results show that it has the highest precision and has no uncertainty results. [Display omitted] • Dynamic forecasting model is proposed for biomass-based hydrogen production yield. • The proposed model is developed based on GBRT-DGM (1, n) algorithm. • The model shows high accuracy and has no uncertain results. • The accuracy of the proposed method is compared with other models. [ABSTRACT FROM AUTHOR]