Accurate monitoring of the crop water status is of great significance for agricultural water management. To address the limitations of traditional spectral models that neglect the synergistic effects of environmental factors, this study aimed to improve the prediction ability of winter wheat water status by integrating multi-source data and machine learning algorithms. The results demonstrated significant improvements in prediction accuracy when environmental factors were integrated with hyperspectral data. During the jointing, heading, and filling stages, the prediction accuracy of the winter wheat plant water content model based on canopy hyperspectral fusion environmental factors (temperature and soil water content) was significantly higher than that based on the canopy spectral data model. The model performance (R²) increased from 0.74, 0.59, and 0.70 to 0.82, 0.69, and 0.76, respectively. The SVM-based full-growth-stage fusion model exhibited superior performance (R² = 0.85, RMSE = 5.10%, RE = 7.79%), achieving accuracy improvements of 3.53%, 23.19%, and 11.84% compared to three key growth-period models. This study confirms that integrating canopy hyperspectral data with environmental factors systematically enhances the generalization capability and accuracy of winter wheat water content prediction, providing a reliable technical solution for precision irrigation and innovative agricultural development in the future.