Wetlands stand out as the largest natural source of atmospheric methane (CH₄), contributing about 30% of total surface CH₄ emissions. Recent assessments, such as those by the Global Carbon Project (GCP), underscore their important role in the global CH₄ budget, albeit with considerable uncertainty. The discrepancy between bottom-up estimates—derived from various modeling approaches—and top-down estimates inferred from atmospheric CH₄ concentrations remains substantial, impeding accurate spatiotemporal understanding and monitoring of wetland CH4 emissions. This work surveys recent advancements in bottom-up estimates of global wetland CH₄ emissions, encompassing a spectrum of methodologies from process-based modeling to data-driven machine learning. Despite progress, significant uncertainties persist, particularly in major tropical and arctic wetland complexes. The limited coverage of data over these regions hampers efforts to refine estimates. Recent breakthroughs in surface measurements of CH₄ fluxes offer promise, yet comprehensive data across diverse wetland ecosystems such as bogs, fens, marshes, and forest swamps remain sparse. Long-term, continuous monitoring coupled with precise wetland mapping and sophisticated modeling frameworks emerges as imperative to enhance global estimates of wetland CH4 emissions.