Hydrogen can be a capable alternative to fossil fuels due to its carbon-free characteristics, in this content, biological hydrogen production is considered a practical approach because technology is green. Due to parameters affecting biohydrogen production, such as operating conditions, it is crucial to predict the process to see the proper yield. There are several conventional and unconventional models used in biohydrogen production prediction. This paper derived a triple first-order prediction model from a previously presented multi-scale kinetic model polynomial built upon the multi-stage growth hypothesis for bio-hydrogen production prediction. The original model was applied to batch and continuous stirred tank reactor studies for their model evaluation, this study evaluated the newly derived model for studies of membrane bioreactors. Due to their increased production yield, membrane bioreactors are an emerging field in biohydrogen production. Although the previous study was mainly applied for batch dark fermentations consisting of various microorganisms, the results presented in this study indicate that it is also applicable for continuous and photo fermentation systems. The original model results reported significant fitness accuracy among different datasheets compared to conventional models like the modified Gompertz model, considering essential factors impacting biohydrogen production suggested in the original model, this paper investigated eleven case studies of dynamic membrane bioreactors with modeling fitness of 99% for most cases. This study reports even higher fitness accuracy compared to the original model, even with different operating conditions.