The ubiquitous oversaturation of methane (CH4) in fluvial environments is hypothesized to be sustained by both proximate and distal factors, which reflect their respective roles in modulating CH4 metabolism through water chemistry as well as hydrological, morphological, and landscape features. Yet, efforts to disentangle their complex interplay in regulating riverine CH4 variability remain limited. Herein, we used machine learning (ML) approach to examine drivers of CH4 variability in five South Asian river basins where CH4 concentration varied widely (0.01–455.75 μmol L–1). Among proximate variables, dissolved oxygen (DO) emerged as the strongest predictor of CH4 concentration, explaining 61–75% of CH4 variability explained by different ML models, followed by total phosphorus (TP) and dissolved organic carbon. Conversely, the extent of built-up area (%) was the key predictor among the distal variables. When combining proximate and distal variables in ML analysis, proximate factors emerged as the dominant drivers, whereas distal factors had only a marginal impact suggesting that local biogeochemical conditions outweigh broader landscape features in determining fluvial CH4 variability. Our ML analysis reveals that while remote-sensing-derived distal variables can assist in predicting CH4 concentrations, they offer limited mechanistic insights. Therefore, integrating proximate factors with landscape variables is important in deriving a comprehensive and mechanistic understanding of CH4 dynamics in river networks.