Biomass gasification is a promising pathway for sustainable hydrogen production, but the vast and varied results in the report on hydrogen content in syngas from the literature hinder process optimization. This study addresses this through a rigorous meta-review, applying a PRISMA methodology and statistical framework to a dataset of 257 unique points from selected studies on bubbling fluidized bed gasifiers between 1996 and 2025. The study quantitatively synthesizes the complex, multi-dimensional parameter space, moving beyond qualitative summaries. Using k-means clustering, heterogeneous feedstocks were classified into distinct biomass categories based on their elemental ratios. Analysis of Covariance (ANCOVA), Response Surface Methodology (RSM), and Random Forest algorithms were employed to model variable effects, identify optimal conditions, and rank their relative importance. Results show steam is the superior gasifying agent. Sensitivity analysis revealed that catalyst category is the most influential variable (importance score = 0.58), followed by temperature and steam-to-biomass ratio. The models identified optimal conditions, predicting an average hydrogen content of 71% for a biomass category with H/C and O/C range of 1.23-1.52 and 0.52-0.75, respectively, using a metal catalyst with CaO. These findings showed that catalyst selection, particularly in sorption-enhanced reforming systems, for maximizing hydrogen yield and advancing efficient biomass-to-hydrogen conversion.