In this study, we aimed to develop a method for the rapid and nondestructive prediction of wheat seed viability using Near-Infrared Spectroscopy (NIRS). Thirteen wheat cultivars were used to establish and validate an NIRS calibration model. The seed samples were divided into a calibration set (n=1,360) and a validation set (n=1,000), representing a wide range of germination rates created through the accelerated aging treatment (98±2% relative humidity, 40°C, 0-10 days). Spectral data were collected within the wavelength range of 400-2,500 nm. Among the three regression models tested, the Modified Partial Least Squares (MPLS) model exhibited the best performance for predicting seed viability, achieving the highest coefficient of determination (R²=0.936) and lowest standard error of calibration (SEC=7.514). The results of this study highlight the utility of NIRS-based models for the rapid, nondestructive assessment of seed viability in wheat. Additionally, this is the first study to apply NIRS for the nondestructive evaluation of wheat seed viability, providing a substantial advancement in seed quality assessment.