In this study a machine learning algorithm was applied in order to develop a predictive model for the changes in phytoplankton biomass (chlorophyll a) in the lower Nakdong River, South Korea. We used a “Hybrid Evolutionary Algorithm (HEA)” which generated model consists of three functions ‘IF-THENELSE’ on the basis of a 15-year, weekly monitored ecological database. We used the average monthly data, 12 years for the training and development of the rule-set model, and the remaining three years of data were used to validate the model performance. Seven hydrological parameters (rainfall, discharge from four multi-purpose dams, the summed dam discharge, and river flow at the study site) were used in the modeling. The HEA selected reasonable parameters among those 7 inputs and optimized the functions for the prediction of phytoplankton biomass during training. The developed model provided accurate predictability on the changes of chlorophyll a (determination coefficients for training data, 0.51; testing data, 0.54). Sensitivity analyses for the model revealed negative relationship between dam discharge and changes in the chlorophyll a concentration. While decreased dam discharge for the testing data was applied; the model returned increased chlorophyll a by 17-95%, and vice versa (a 3-18% decrease). The results indicate the importance of water flow regulation as specific dam discharge is effective to chlorophyll a concentration in the lower Nakdong River.