Cotton maps for Telangana which is one of the cotton producing region in India were produced through supervised classification in Google Earth Engine. To make well-informed decisions, farmers, governments, scientists, and agricultural organizations need accurate information on crop prediction. However, automated crop type mapping remains challenging due to the limited availability of field-level crop labels required to train supervised classification models. Cotton mapping was made more accurate and efficient by using a two-step mapping approach, which consists of mapping the cropland and then extracting the cotton crop for areas with more heterogeneity, this framework increased the accuracy from 83% to 91%. For a more accurate estimate of the cotton crop, this study combined high resolution Sentinel-1 and Sentinel-2 data with several secondary data types in the SMILE Random Forest (RF) model at various stages of the crop growth season. For that First, cropland/non-cropland area were predicted to extract features from time series. Next, cotton crops through RF classifiers were applied on median composites of Sentinel-1 and Sentinel-2 data for each pixel in the region. Furthermore, spectral, structural and phenological feature time-series satellite data were merged and processed into a supervised random forest classifier. The classification of cotton, cropland and non-cropland model produced with producers accuracy of 98%, 88% and 90%. Through experiments, we also discovered that employing time-series imagery generates substantially higher classification results than single-period images. The inclusion of shortwave infrared bands, followed by the addition of red-edge bands, can increase crop classification accuracy more than using simply traditional bands like the visible and near-infrared bands. Incorporating common vegetation indices and Sentinel-2 data, combining with Sentinel-1 reflectance bands improved the overall crop classification accuracy by 0.2% and 0.6%, respectively. This study demonstrates how combining optical and microwave remote sensing data, the GEE platform, transfer learning, and cotton cropland mapping algorithms can enhance insights into precision agricultural systems.