Based on atmospheric-hydrological data and satellite remote sensing data from 2014 to 2018, the support vector machine(SVM), long short-term memory model(LSTM), extreme gradient boosting(XGBoost)model were applied to predict the cyanobacterial bloom area in fields including the whole region, Gonghu Bay, southern coastal region and central north-western region of Taihu Lake. The results demonstrated that the XGBoost regression model had better accuracy than SVM and LSTM regression model in the whole region and subdivided regions. Compared with the observed cyanobacterial bloom area, simulated areas of SVM regression model and XGBoost regression model were lower in the Taihu Lake under different time scales, while the development tendency of cyanobacterial blooms was effectively simulated. In addition, the XGBoost classification model had better accuracy than SVM and LSTM classification model for the whole region and the central north-western region of Taihu Lake. Three classification models had high accuracy in the Gonghu Bay and the southern coastal region of Taihu Lake. Finally, taking the atmospheric-hydrological data and water quality data of the same day and one day advanced as model inputs, the XGBoost regression model has high accuracy and robustness in cyanobacterial bloom area simulation, which had a promising application prospect for the cyanobacterial bloom prediction.