Cotton is the world's most widely cultivated fiber crop and holds great significance unsuitable planting environments can hinder farmer income and result in a substantial resources.This study explores suitability of cotton planting areas in Xinjiang to reduce agricultural pollution. The goal is to promote sustainable agricultural development by considering both soil fertility, factors often overlooked in previous research. We analyzed climate change used machine learning-transfer component analysis to build a transferable coupling model (TN) and soil organic carbon (SOC) indicators, resulting in a cotton suitability zoning that and soil fertility factors. Xinjiang has seen an overall increase in cumulative temperature southern Xinjiang showing the most significant rise (4.02% in temperature and 16.26% in forest model (RF) outperformed multivariate linear regression (MLR) and support vector predicting soil fertility indicators (TN: R2 = 0.80, SOC: R2 = 0.77). The RF-TCA coupling adaptability, with better performance in TN prediction compared to SOC. The Xinjiang zoning, based on meteorological and soil data, indicates a northward shift in suitable in northern Xinjiang, while southern Xinjiang continues to maintain a substantial number zones. Notably, the disparity in suitability between the two regions has been narrowing over offers valuable insights for optimizing cotton planting locations, enhancing resource efficiency, sustainable development in Xinjiang.