The water quality in estuaries is influenced by both physical and biochemical processes, making it difficult to identify and quantify the main controlling factors of chlorophyll-a (Chl-a) concentration. This study introduces an interpretable machine learning approach to identify and quantify the dominant control factors determining Chl-a concentration in the Yellow River Estuary (YRE). The model utilizes in situ data and MODIS data in the Yellow River Estuary, incorporating five key variables: salinity, water depth, turbidity, dissolved inorganic nitrogen (DIN), and soluble reactive phosphorus (SRP). Three types of inputs are used to predict Chl-a, including physical, biogeochemical, and physical-biogeochemical factors. The model further uses feature importance ranking and partial dependence plot to identify and quantify key control factors. The results reveal that (1) Chl-a variation is influenced by multiple factors, especially salinity and turbidity; (2) Chl-a concentration is inhibited when salinity is below 26, turbidity is lower than 16 NTU and above 30 NTU, DIN is lower than 0.314 mg/L and SRP is below 0.01 mg/L; (3) riverine inputs change turbidity and salinity in the Yellow River Estuary and Laizhou Bay, in response to temporal-spatial distributions of phytoplankton. This research can provide a valuable reference for water quality management in the Yellow River Estuary.