Abstract: Protein content is a critical indicator for evaluating the nutritional value and quality of hazelnuts. Consequently, developing a robust detection model applicable across multiple production regions is essential for the standardization and sustainable development of the hazelnut industry. In this study, hyperspectral imaging (400~1000 nm) was utilized to acquire data from hazelnut samples collected in Hulin, the Greater Khingan Mountains, and Yichun. Raw spectra were preprocessed using Savitzky–Golay smoothing combined with the first derivative to mitigate noise and enhance spectral features. Subsequently, a hybrid modeling framework integrating the Tornado Optimizer with Coriolis force and a Backpropagation Neural Network was developed and benchmarked against models based on Genetic Algorithm and Particle Swarm Optimization. The results indicated that the model based on the Tornado Optimizer with Coriolis force exhibited the best performance in terms of the coefficient of determination and root mean square error on test sets from different regions. The coefficient of determination of the global universal model was 0.7839, and the root mean square error was 0.0281, demonstrating strong cross-regional applicability. To further enhance model robustness and feature expression efficiency, the Competitive Adaptive Reweighted Sampling algorithm was employed for spectral feature optimization and selection. This effectively eliminated redundant wavelengths, increasing the coefficient of determination of the optimized universal model to 0.8297 (an improvement of 5.84%) and reducing the root mean square error to 0.0199 (a reduction of 29.2%), thereby significantly enhancing the detection accuracy and generalization ability of the model. In conclusion, the proposed approach facilitates the rapid, non-destructive, and high-precision quantification of hazelnut protein content across multiple regions, providing reliable technical support for quality assessment in the hazelnut industry.