With the goal of low-cost and high-accuracy estimation of Chlorophyll-a (Chl-a), a model for estimating Chl-a in the surface layer of the Western South China Sea (WSCS) was constructed in this study. Using the data from the WSCS cruises in the past ten years, based on the influence and contribution of hydro-meteorology conditions to the oceanic biochemical processes, the Hydro-Meteorological Parameters (HMPs) were used as the input data of Random Forest (RF) algorithm. To evaluate the reliability of estimating Chl-a based on HMPs, the QAA algorithm was used to derive the in-situ remote sensing reflectance (R_rs) based on the measured inherent optical property parameters. Then Chl-a was estimated and evaluated by the combination of classical empirical algorithms for water color products such as OC4, Aiken and Tassan, the evaluation results showed that the OC4 algorithm had the highest estimation accuracy, with an R² of up to 0.438. The comparison with the R² of 0.568 of the RF-based model shows that, owing to the large data volume of HMPs, the Chl-a estimation results of the RF model based on HMPs show more excellent stability and generalization, and better spatial distribution consistency with the measured results. The importance of feature parameters was found that in the machine learning model for estimating Chl-a based on HMPs, salinity is the most important feature variable, followed by temperature, wind and air pressure in that order, and the lowest contributor is relative humidity.