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Aiming to promote the application of machine learning in agriculture field and improve accuracy of the maize yield estimation, the central plain of Hebei Province was selected as the study area, which includes fifty-three counties (districts). Vegetation temperature condition index (VTCI) and leaf area index (LAI）at the main growth stages of maize were selected as key crop growth indicators for estimating the maize yield by using two machine learning methods, extreme gradient boosting (XGBoost) and random forest (RF), and as well as their combination. Firstly, the XGBoost and RF were used to estimate yield of maize from 2010 to 2017, then the XGBoost and RF’s weights were determined by combination forecasting model by using the Shapley value method, and finally maize yield of each county in 2012 was estimated based on the combination forecasting model. The results showed that the mean relative error (MRE) and root mean square error (RMSE) between the estimated yield of maize and the actual yield were 9.86% and 831.14kg/km2, respectively. The accuracy of the combination forecasting model (R2=0.52, P<0.001) was better than that of the XGBoost model and RF model, which can be applied to estimate the yield of maize in the study area. The combination model was used to estimate the maize yield of the central plain of Hebei Province pixel by pixel from 2010 to 2018. The estimated yield of maize showed a trend of decrease first and then increase over time. The spatial distribution of maize yield was the highest in the western region, followed by the northern and southern regions, and the eastern region was the lowest. The results showed that the temporal and spatial changes of maize in the central plain of Hebei Province were in line with reality, and the research result can provide guidance for the growth monitoring and yield estimation of maize in the study area.