[Objective] This study tries to explore the practicability and effectiveness of forecasting public health risks with machine learning, taken influenza as an example. [Methods] First, we collected the data on influenza and meteorological factors during 2009 to 2016 in Lanzhou, China. Data from the year 2009 to 2015 were used as the training data and 2016 as the testing data. Then, based on SARIMA, Kalman Filter, and VAR, three machine learning methods for influenza prediction were put forward, respectively. Moreover, we designed two multi-method combined forecasting strategies. Finally, the forecasting performance of the above methods (strategies) was carefully evaluated and compared. [Results] The SARIMA, VAR, and Kalman Filter achieved best predict performance in the whole period (WP), outbreak period (OP), and stabilization period (SP), with RMSE at 11.68, 19.23, 1.60, and R2 at 0.932, 0.923, 0.956, respectively. The forecasting performance among all three scenarios was improved by our multi-method combined strategies, in which Comb_2 has better performance, with RMSE at 10.82, 14.68, 1.38, and R2 at 0.942, 0.934, 0.963, respectively. [Limitations] Limited by the data, this study just considered meteorology factors as external factors. [Conclusions] Predicting public health risks (such as influenza) with machine learning is practicable, effective and has great potential. But a lack of multi-source data is the major dilemma. Therefore, to promote the open exchange and sharing of data, barriers should be broken at the technical, organizational, and institutional levels.