Corn is one of the important grain reserve crops in China, and its yield directly impacts national food security. The chlorophyll content of corn is closely related to its photosynthetic capacity and significantly affects the photosynthetic rate of the leaves and vegetation productivity. It is an important crop parameter for monitoring crop growth, pest and disease surveillance, and maturity prediction. Real-time and accurate monitoring is of great significance for corn parameters and yield prediction. This study was conducted in the typical black soil area of Lishu County, Siping City, Jilin Province. To solve the problem of missing effective images that may occur during the revisit period of Sentinel-2 satellites, a method for retrieving corn leaf chlorophyll based on the fusion data of Sentinel-2 and MODIS images was proposed. Using fused imagery, three machine learning algorithms were employed: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBOOST) to construct a model for estimating corn leaf chlorophyll content, and the accuracy of the model was verified. The conclusions obtained were as follows. The data simulated using the ESTARFM data fusion algorithm maintained a high correlation with the real imagery. Among the leaf chlorophyll inversion models for missing image dates, where input variables included fused image band reflectance and vegetation index, the XGBOOST model showed good fitting accuracy The research demonstrates that accurate estimation of leaf chlorophyll content can be achieved even on days with missing imagery, when fusion image feature bands are integrated with machine learning algorithms. This notably improves the temporal precision of corn chlorophyll content measurement, presenting a novel method for daily or large-scale inversion studies of leaf chlorophyll content, particularly in scenarios involving image gaps. Furthermore, it illuminates the potential for refined monitoring of physiological and biochemical parameters across a wider range of crops, with shortened time intervals.