The aim of this study is to develop a rapid method for monitoring leaf nitrogen content (LNC) in winter wheat, which is essential for precise field management and accurate crop growth assessment. This study used a natural winter wheat population at Shanxi Agricultural University’s experimental base as the subject. UAV-mounted multispectral sensors collected images at jointing, heading, pre-grouting, and late grouting stages. Canopy spectral reflectance was extracted using image segmentation, and vegetation indices were calculated. Correlation analysis identified highly relevant indices with LNC. Support Vector Regression (SVR), Random Forest (RF), Ridge Regression (RR), K-Nearest Neighbors (K-NN), and ensemble learning algorithms (Voting and Stacking) were employed to model the relationship between selected vegetation indices and LNC. Model performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). Results showed that the Voting-based ensemble learning model outperformed other models. At the pre-grouting stage, this model achieved an R² of 0.85 and an RMSE of 1.57 for the training set, and an R² of 0.82 and an RMSE of 1.64 for the testing set. This study provides a theoretical basis and technical reference for monitoring LNC in winter wheat at key growth stages using low-altitude multispectral sensors, supporting precision agriculture and variety evaluation.