Urban　parks　are　critical　for　mitigating　environmental　challenges;　however,　their　beneficial　impacts　on　eco-environmental　quality　(EEQ)　have　not　been　comprehensively　explored.　Therefore,　we　developed　a　new　method　of　quantifying　EEQ　improvement　and　driving　factors　for　51　urban　parks　in　Fuzhou,　China.　Our　multimethod　approach　combined　remote　sensing,　geospatial　analysis,　and　interpretable　machine　learning　models　to　evaluate　four　park　EEQ　improvement　indicators:　park　EEQ　improvement　intensity　(PEII),　distance　(PEID),　area　(PEIA),　and　efficiency　(PEIE)　and　unravel　the　nonlinear　interactions　among　internal　and　external　environmental　factors.　According　to　the　results,　the　degree　of　EEQ　improvement　varied　significantly　between　parks.　PEII　ranged　from　−0.0773　to　0.3095　(mean　=　0.1425)　and　parks　exhibited　different　trends　between　PEII　and　PEIE.　Key　internal　factors　such　as　park　area　(PA),　perimeter,　and　aggregation　index　were　positively　correlated　with　PEII,　PEID,　and　PEIA　but　negatively　correlated　with　PEIE,　whereas　edge　density　showed　inverse　correlations.　XGBoost　and　SHAP　analyses　highlighted　nonlinear　relationships,　with　PA　emerging　as　the　most　influential　factor　in　PEIE.　A　novel　nonlinear　threshold　of　2.15　hm²　was　identified　as　the　optimal　PA　for　balancing　PEIE　and　land-use　efficiency,　beyond　which　ecological　efficiency　declined.　These　findings　highlight　the　complexity　of　park-driven　EEQ　improvements　shaped　by　interactions　between　park　attributes　and　external　environmental　factors.　This　study　provides　actionable　insights　for　urban　planners　to　optimize　park　design　and　management,　emphasizes　the　need　for　balanced　scaling　and　connectivity　to　enhance　ecological　benefits,　and　offers　a　model　for　data-driven,　context-specific　greening　strategies　in　rapidly　urbanizing　regions.　©　2025　Elsevier　Ltd