With the progress of Chinese dual-carbon strategy, scientifically assessing greenhouse gas (GHG) emission characteristics from reservoirs is essential for objectively evaluating the climate effects of hydropower projects and supporting the low-carbon attributes of the hydropower sector. Riverine reservoirs integrate longitudinal riverine transport, reservoir retention and sedimentation, and engineering regulation. Their greenhouse gas emissions are controlled not only by internal carbon transformation processes, but also by reservoir impoundment, water-level regulation, dam-discharge degassing, and alternating wetting and drying processes in water-level fluctuation zones. Identifying emission pathways, dynamic variations, and upscaling approaches for reservoir greenhouse gases is therefore critical for evaluating the climate effects and low-carbon attributes of riverine hydropower reservoirs. This review focuses on the identification of major riverine reservoir greenhouse gas emission pathways and related assessment requirements. It systematically summarizes key processes and monitoring needs, including carbon pulses during impoundment, reservoir-operation-driven source-sink shifts, air-water interface diffusion, ebullition, degassing during dam discharge, and non-steady emissions from water-level fluctuation zone. The applicability and limitations of major monitoring techniques, including chamber methods, thin boundary layer models, eddy covariance, acoustic sensing, and remote sensing–based spatial identification, are further reviewed. Taking the Three Gorges Reservoir as a representative case, this paper summarizes the practical evolution and major challenges of greenhouse gas monitoring technologies in large riverine reservoirs. The synthesis indicates that current riverine reservoir greenhouse gas monitoring still faces several challenges, including insufficient resolution of coupled emission processes, unclear interfaces among different methods, high uncertainty in key emission pathways, difficulties in characterizing non-steady emissions from water-level fluctuation zones, and limited capacity for multi-scale data integration. Future work should shift from single-method applications toward monitoring-target-driven method combinations, build multi-parameter collaborative monitoring networks and standardized datasets, strengthen remote sensing-based spatial constraints and mechanistic constraints, and promote the integration of process-based models with data-driven approaches. These developments will provide methodological support for whole-reservoir dynamic assessment, carbon footprint accounting, and low-carbon operation management of large riverine reservoirs.