In this study, a neural network (NN) emulator for radiation parameterization was developed for the use of an operational weather forecasting model in the Korea Meteorological Administration. The development of the NN emulator was based on large-scale training sets and 96 categories (longwave–shortwave, months, land–ocean, and clear–cloud). As the radiation parameterization was replaced by the NN emulator, a 60-fold speedup for the radiation process was achieved, with a decrease of 87.26% in the total computation time. The accuracy of the NN emulator was strictly evaluated through comparison with the results obtained from the infrequent use of the original radiation scheme with the same computational cost. The mean errors of the NN radiation emulator were significantly reduced by 21–34% compared with the infrequent method. The combination of using the NN radiation emulator and applying it infrequently provided an additional speedup of up to 36-fold, corresponding to 2180 times speedup compared with the control run, without a significant reduction in accuracy. The optimized structure for the radiation emulator designed in this study also showed universal robustness even in the use of limited training sets with incomplete coverage. In conclusion, the NN radiation emulator in this study provides benefits regarding both accuracy and computational cost, making it useful for improving weather forecasting modeling.