Under　the　dual　pressures　of　global　climate　change　and　rapid　urbanization,　urban　drainage　systems　(UDS)　face　severe　challenges　caused　by　extreme　precipitation　events　and　altered　surface　hydrological　processes.　The　drainage　paradigm　is　shifting　toward　resilient　systems　integrating　grey　and　green　infrastructure,　necessitating　a　comprehensive　review　of　the　design　and　operation　of　grey　infrastructure.　This　study　systematically　summarizes　advances　in　urban　stormwater　process-wide　regulation,　focusing　on　drainage　network　design　optimization,　siting　and　control　strategies　for　flow　control　devices　(FCDs),　and　coordinated　management　of　Quasi-Detention　Basins　(QDBs).　Through　graph　theory-driven　topological　design,　real-time　control　(RTC)　technologies,　and　multi-objective　optimization　algorithms　(e.g.,　genetic　algorithms,　particle　swarm　optimization),　the　research　demonstrates　that　decentralized　network　layouts,　dynamic　gate　regulation,　and　stormwater　resource　utilization　significantly　enhance　system　resilience　and　storage　redundancy.　Additionally,　deep　learning　applications　in　flow　prediction,　flood　assessment,　and　intelligent　control　exhibit　potential　to　overcome　limitations　of　traditional　models.　Future　research　should　prioritize　improving　computational　efficiency,　optimizing　hybrid　infrastructure　synergies,　and　integrating　deep　learning　with　RTC　to　establish　more　resilient　and　adaptive　urban　stormwater　management　frameworks.　©　2025　by　the　authors.