Assessing the acreage for crop classification using optical satellite data encounters difficulties in India's rainy season because of continuous cloud cover. Microwave SAR data offers a potential alternative solution, and this research employed an efficient blend of multi-date Sentinel-1 SAR data. During the winter rice season of 2022-23, six sets of SAR data from different dates between June and August were examined in the Ribhoi district of Meghalaya. VH polarization was chosen for classification due to its greater consistency and reliability compared to VV polarization. Supervised machine learning classification algorithms, particularly support vector machine, were implemented for classification using ArcGIS software. The accuracy of this technique was assessed by comparing it with ground truth data, yielding an overall accuracy of 87% with a kappa coefficient of 0.73. Additionally, the estimated winter rice area from SAR images closely matched the reported area (9327 ha) from the State Department of Agriculture under rice cultivation in Ri-bhoi district, confirming result consistency.