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In order to investigate the simulation effect of machine learning model on actual evapotranspiration (ETa) of winter wheat during the reproductive period and the effect of solar-induced chlorophyll fluorescence (SIF) on the simulation accuracy of machine learning model in the absence of meteorological data, SIF was combined with meteorological indicators, crop physiological indicators, soil thermal conditions and other factors, and three classical machine learning models, namely the gradient boosting (GB), random forest (RF), and support vector machine (SVM) were constructed, combined with linear regression (LR) model to simulate winter wheat ETa and compared with the evapotranspiration ET_pm calculated by Penman-Monteith (P-M) model. The results showed that although SIF was significantly correlated with ETa, the fitting accuracy of the machine learning model constructed only by using SIF as a feature parameter was low; according to the importance ranking of the feature parameters based on the machine learning model as well as the simulation accuracy of the model under each scenario, it was known that SIF had an enhancement effect on the accuracy of the machine learning model in simulating ETa. The machine learning model fit better than the P-M model when there were enough feature parameters, and adding feature parameters to the average temperature, SIF, sunshine hours, leaf area index (LAI) and soil moisture content did not improve the simulation accuracy, so it was recommended to use the feature set composed of the five feature parameters mentioned above to construct a machine learning model to predict ETa. The R2 of the models were 0.92, 0.91 and 0.91, respectively, among which the GB model had the best fitting effect on the ETa of winter wheat during the whole reproductive period. The research result can provide a reference for the accurate simulation of local evapotranspiration and the development of rational irrigation system in the absence of meteorological data.