[Objective] Building an accurate and effective forecasting model for major infectious diseases based on multi-machine learning can predict outbreak trends and help formulate countermeasures in advance.

[Methods] Based on the Gray Wolf Optimization algorithm, three machine learning optimal weight combinations of ANFIS, LSSVM and LSTM are searched to establish an ensemble prediction model. Experiments are designed to assess the model prediction performance by COVID-19 epidemic data.

[Results] The results show that ANFIS, LSSVM, and LSTM were suitable for confirmed case, death case, and recovery case scenarios, respectively; the R2 of the ensemble prediction model based on Gray Wolf Optimization reached 0.987, 0.993, and 0.987 for the three scenarios. The average RMSE was reduced by 38.79%, 64.40%, and 53.88% compared to the single model, respectively.

[Limitations] The model needs to be further verified by using other major infectious disease epidemic data sets.

[Conclusions] Different machine learning models have their own prediction performance, and the ensemble prediction model based on Gray Wolf Optimization can effectively merge the advantages of multiple machine learning models to obtain stable and accurate prediction results.