Abstract: Rice is one of the most important staple crops worldwide, with China being the largest rice producer. Rice yield and nitrogen use efficiency (NUE) are critical factors determining food security and agricultural sustainability. Nitrogen (N) is an essential nutrient for rice growth; however, excessive nitrogen fertilizer application reduces NUE, increases production costs, and causes environmental pollution. Therefore, accurately predicting rice yield and NUE is crucial for optimizing nitrogen management, improving rice production efficiency, and mitigating environmental impacts. In recent years, the development of remote sensing technology, particularly the application of unmanned aerial vehicle (UAV) multispectral imagery, has significantly enhanced the precision management of agricultural production. UAV remote sensing technology enables efficient acquisition of crop growth status information and, when combined with machine learning approaches, facilitates the construction of accurate predictive models, thereby providing data support and decision-making assistance for agricultural production.A two-year field experiment was conducted in Fengyang County, Anhui Province, China, involving multiple nitrogen levels and rice varieties. UAV multispectral images were collected at three key growth stages (tillering, heading, and grain filling stages) to obtain remote sensing data. The recursive feature elimination (RFE) algorithm was used to select sensitive vegetation indices (VIs), texture features (TFs), and their combined features. Six machine learning models-random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), gradient boosting machine (GBM), deep neural network (DNN), and long short-term memory (LSTM)-were employed to establish direct prediction models based on “sensitive remote sensing features–yield and NUE” Additionally, an indirect prediction model based on “sensitive remote sensing features–yield–NUE” was proposed using calculated features. The potential of UAV remote sensing technology for accurate NUE prediction was evaluated by comparing the performance of direct and indirect models.The results showed that: (1) The prediction of rice yield and NUE at different growth stages exhibited varying dependencies on remote sensing features. At the tillering stage, spectral-based vegetation indices contributed more to prediction accuracy, whereas at the grain filling stage, texture features played a more significant role in NUE prediction. Difference vegetation index (DVI), visible atmospherically resistant indices (VARI), and modified normalized difference blue index (mNDblue), as well as the texture mean (Mean), were sensitive to yield across multiple growth stages. Similarly, reciprocal ratio vegetation index (repRVI), correlation (Cor), and Mean were consistently sensitive to NUE. (2) Among all models, the deep neural network (DNN) achieved the highest accuracy in direct predictions of yield and NUE. At the grain filling stage, the TF-based model provided the most accurate yield predictions (R²= 0.938, RMSE = 479.591 kg/hm²). At the tillering stage, the hybrid feature-based model demonstrated the best predictive performance for NUE, with agronomic nitrogen use efficiency (aNUE) and nitrogen partial factor productivity (NPFP) achieving R² values of 0.711 (RMSE = 4.448 kg/kg) and 0.781 (RMSE = 12.787 kg/kg), respectively. (3) Compared to direct models, the indirect model significantly improved NUE prediction accuracy. The R² values for aNUE and NPFP increased by 18.589% and 14.733%, while RMSE values decreased by 54.411% and 90.015%, respectively. The proposed indirect prediction framework for NUE overcomes the limitations of traditional methods that rely on data from the maturity stage, enabling nondestructive and dynamic assessment at earlier growth stages. This study demonstrates that UAV multispectral imagery combined with machine learning techniques effectively enhances the prediction accuracy of rice yield and NUE. The DNN model outperforms traditional machine learning models in predictive accuracy, and the indirect prediction method surpasses the direct approach. These findings provide a novel methodology for precision management in rice production and offer valuable technical support for intelligent agricultural management.