Taking Taihu Lake as an example, a prediction model of chlorophyll a concentration based on extreme learning machine (ELM) model was constructed for the first time using the remote sensing images of Environment 1. The prediction results were then compared with those of the traditional BP artificial neural network and support vector machine (SVM) model. It was found that R2 of ELM model reached as high as 0.911 4; however it only reached 0.366 3 of BP and 0.744 8 of SVM model. Root mean squared error (RMSE) of BP model and SVM model reduced from 3.728 8 μg/L and 2.132 4 μg/L to 1.3270 μg/L of ELM model. The mean relative error (MRE) of ELM model was 2.65%, which was lower than that of BP model (6.59%) and SVM model (3.89%). Compared with these two models, the ELM model has higher accuracy in the back analysis of chlorophyll a concentration in Taihu Lake. The parameter selection of ELM model is simple, which can significantly improve the learning speed of the model. This model has better generalization performance and it is not prone to local optimum. The experiment results show that ELM model can be effectively applied to the prediction of chlorophyll a concentration in inland lakes.