Google Earth Engine (GEE) has revolutionized remote sensing. The GEE cloud platform lets users quickly analyze large satellite imagery datasets with custom programs, enhancing global-scale analysis. Crop condition monitoring using GEE would greatly help in decision-making and precision agriculture. Estimating canopy chlorophyll content (CCC) is an effective method for monitoring crops through remote sensing because chlorophyll in leaves is a key indicator. A hybrid model that combines radiative transfer models (RTMs), such as PROSAIL, with Gaussian Process Regression (GPR) can effectively estimate crop biophysical parameters using remote sensing images. GPR has proven to be one of the best methods for this purpose. This study aimed to develop a hybrid model to estimate CCC from S2 imagery and transfer it to the GEE platform for efficient data processing. In this work, the CCC (g/cm2) data from the S2 biophysical processor toolbox for the S2 imagery of ICAR-Indian Agricultural Research Institute (IARI) on February 23rd, 2023, was used as observation data to train the hybrid algorithm. The hybrid model was successfully validated against the 155 input data with an R2 of 0.94, RMSE of 10.02, and NRMSE of 5.04%. The model was integrated into GEE to successfully create a CCC estimated map of IARI using S2 imagery from February 23, 2023. An R2 value of 0.96 was observed when GEE-estimated CCC values were compared against CCC values estimated locally. This establishes that the GEE-based CCC estimation with the PROSAIL+GPR hybrid model is an effective and accurate method for monitoring vegetation and crop conditions over large areas and extended periods.