This paper proposes an approach for detecting changes in cropping patterns using Sentinel-2 data and Google Earth Engine (GEE) in the Ramappa command area, an important agricultural region in India. Cropping pattern changes can indicate various factors such as changes in land use, climate, and management practices, and timely detection of these changes is essential for effective agricultural monitoring and management. The proposed approach involves pre-processing of Sentinel-2 data in GEE, cloud masking, and image compositing. Then, temporal analysis is performed by comparing the NDVI classified images acquired in different time periods (2019–2022) to detect changes in cropping patterns. A combination of spectral indices matching and machine learning algorithms is used to enhance the accuracy of the analysis. The proposed approach is tested in the Ramappa command area, and the results show that it can effectively detect changes in cropping patterns. The results obtained are compared with the open source crop area statistics available from Water Resources Information System (WRIS), by NRSC Bhuvan. The proposed methodology has the potential to be applied in other agricultural regions for change detection and monitoring. The use of Sentinel-2 data and GEE makes the approach scalable and cost-effective. The results can be used for timely decision-making, resource management, and land use planning in the Ramappa command area. The paper provides a valuable contribution to the field of remote sensing and agricultural monitoring, especially in the context of detecting changes in cropping patterns in the Ramappa command area.