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This paper analyzes the relationship between mosquito density and meteorological conditions from 2009 to 2019 in Beijing and its 14 districts based on three machine learning methods. The result shows that the mosquito density fluctuates periodically from May to October each year. The average is between 0.35 to 2.54 per lamp·hour, and the peak appears in mid-July to mid-August, corresponding to the period of highest temperature and most precipitation in Beijing. We choose Multiple Regression, Support Vector Machine and Random Forest to predict the mosquito density for ten days with different input factors. RMSE and MAPE are used to test the prediction effect. It turns out that it is relatively better in areas where the mosquito density is stable, such as Pinggu, Mentougou, Daxing, Haidian and so on. In addition, among the three methods, the support Vector Machine Method has a very good prediction effect in late May 2019, while the prediction effect of Multiple Regression and the Random Forest is more stable from May to October 2019.