Estimating chlorophyll-a concentration (Chla) in global inland waters is challenged by their diverse material compositions and complex optical properties. Single Chla models often lack universality and may not adequately address the needs of large-scale, diverse water bodies. We collected remote sensing reflectance and Chla data from 23 (n=1383) inland and offshore water bodies worldwide. Following Neil et al.’s classification methodology, we categorized the collected data. Then, we conducted algorithm selection and parameter optimization for Chla estimation in 10 different types of water bodies, focusing on the application of the Sentinel-3 Ocean and Land Color Instrument sensor. After validation with measured data, our models achieved a notable R2 value of 0.93, with a root mean square error of 6.25 (mg/m3) and an average unbiased relative error of 30.10%. These results indicate a significant enhancement in algorithm accuracy, demonstrating wide applicability across complex water bodies globally.