As urbanization accelerates, understanding and managing carbon emissions from urban sewer networks have become crucial for sustainable urban water cycles. This review examines the factors influencing greenhouse gas (GHG) emissions within urban sewage systems, analyzing the complex effects between water quality, hydrodynamics, and sewer infrastructure on GHG production and emission processes. It reveals significant spatiotemporal heterogeneity in GHG emissions, particularly under long-term scenarios where flow rates and temperatures exhibit strong impacts and correlations. Given the presence of fugitive and dissolved potential GHGs, standardized monitoring and accounting methods are deemed essential. Advanced modeling techniques emerge as crucial tools for large-scale carbon emission prediction and management. The review identifies that traditional definitions and computational frameworks for carbon emission boundaries fail to fully consider the inherent heterogeneity of sewers and the dynamic changes and impacts of multi-source pollution within the sewer system during the urban water cycle. This includes irregular fugitive emissions, the influence of stormwater systems, climate change, geographical features, sewer design, and the impacts of food waste and antibiotics. Key strategies for emission management are discussed, focusing on the need for careful consideration of approaches that might inadvertently increase global emissions, such as ventilation, chemical treatments, and water management practices. The review advocates for an overarching strategy that encompasses a holistic view of carbon emissions, stressing the importance of refined emission boundary definitions, novel accounting practices, and comprehensive management schemes in line with the water treatment sector's move towards carbon neutrality. It champions the adoption of interdisciplinary, technologically advanced solutions to mitigate pollution and reduce carbon emissions, emphasizing the importance of integrating cross-scale issues and other environmentally friendly measures in future research directions.