Nitrogen is one of the important elements for the growth and development of rice, and accurate estimation of nitrogen concentration is crucial for guiding precise fertilization and assisting in the selection of nitrogen efficient varieties in rice. Traditional field sampling methods make it difficult to obtain real-time nitrogen concentration in rice. With the rapid development of information technology, establishing the relationship between unmanned aerial vehicle hyperspectral data and nitrogen concentration through machine learning methods is currently one of the main technical routes for crop nitrogen nutrition diagnosis. This study constructs an inversion model by using the feature bands of unmanned aerial vehicle canopy hyperspectral data selected by the continuous projection algorithm as input and the measured nitrogen concentration data as output. Extreme learning machine (ELM) has the advantages of fast speed and strong generalization ability compared to similar machine learning methods. However, due to its randomly generated connection weights and neuron thresholds, its training stability is insufficient and it is prone to falling into local optima. The beluga whale optimization (BWO) is a competitive algorithm inspired the behavior of beluga whales to solve single modal and multimodal optimization problems. In this study, the BWO-ELM rice nitrogen concentration unmanned aerial vehicle hyperspectral inversion model was constructed to achieve rapid estimation of rice nitrogen concentration by optimizing the connection weight between the input layer and the hidden layer of the ELM, as well as the initial weight of the hidden layer through the BWO. The research results show that the continuous projection algorithm filters out 10 feature bands, which are 673, 703, 727, 823, 850, 877, 895, 952, 961, and 985 nm, respectively. The training set R² and RMSE of the nitrogen concentration inversion model constructed based on BWO-ELM are 0.742 5 and 0.382 6%, respectively, while the testing set R² and RMSE are 0.702 8 and 0.487 7%, respectively. The predictive ability is superior to that of the nitrogen concentration inversion model constructed based on ELM. In summary, the rice nitrogen concentration unmanned aerial vehicle hyperspectral inversion model based on BWO-ELM can quickly and accurately obtain rice nitrogen concentration, providing a new method for rice nutrition monitoring.