Wetlands are the largest natural source of atmospheric methane (CH₄), accounting for approximately 30% of total surface CH₄ emissions. Recent evaluations, including those by the Global Carbon Project (GCP), underscore their significant role in the global CH₄ budget, though with substantial uncertainty. The discrepancy among bottom-up estimates—derived from various modeling approaches—remains considerable, hindering accurate spatiotemporal understanding and monitoring of wetland CH₄ emissions. In this talk, I first synthesize recent advancements in bottom-up modeling of global wetland CH₄ emissions, encompassing a range of methodologies from process-based modeling to data-driven machine learning. Despite progress, significant uncertainties persist, particularly in major tropical and arctic wetland complexes. Additionally, I introduce emerging hybrid solutions that combine state-of-the-art machine learning architectures, process-based models, physical knowledge, and the growing surface measurements of CH₄ fluxes to address the substantial uncertainty in global wetland CH₄ emission estimates.