The Scomberomorus niphonius is an economically important large-scale pelagic fish in the coastal waters of China. The accurate prediction of the fishing distribution of S. niphonius is of great significance for the assessment and management of fishery resources. The development of satellite remote sensing and artificial intelligence has provided strong technical support and convenience for fishery forecasting research. In this study, a S. niphonius CPUE prediction model based on automatic machine learning was constructed using S. niphonius fishing data, satellite remote sensing sea surface temperature (SST), and sea surface chlorophyll-a concentration (Chl-a) data. Through comparison with the XGBoost, random forest, and generalized additive models (GAM), the coefficient of determination (R² ) of the automatic machine learning model increased by 51%, 107%, and 117%, respectively, and the root mean square error (RMSE) was reduced by 15%, 28%, and 32%, respectively. A fishery abundance index was developed through the CPUE predicted by the model, and the temporal and spatial variation laws of fishery abundance were analyzed. The results showed that the high-abundance area of S. niphonius presented a trend in moving northward and nearshore because of the influence of SST in spring, which is consistent with the feeding and spawning migration route of S. niphonius. The northward shift of the latitudinal center of gravity in the high-abundance area for the S. niphonius fishery was related to the change in the suitable temperature area for feeding and spawning of the S. niphonius under the influence of climate warming. Through the analysis of the climate event index, it was found that the Ni?o 3.4 index was significantly correlated with the area of high abundance of S. niphonius fishery (r=0.58, P＜0.05). Combined with RCP2.6, RCP4.5, RCP6.0, and RCP8.5 for the four scenarios, the distribution changes in the CPUE of S. niphonius by 2100 were predicted. It was determined that with global warming, the high-value production area of CPUE of S. niphonius will continue to move northward, and increase by 0.1 kg/h, 2.2 kg/h, 2.4 kg/h, and 17.3 kg/h, respectively, compared with the predicted average value of CPUE of S. niphonius from 2010 to 2015. The results of this study provide a reference for predicting the fishing conditions of economically important fish species in the coastal waters of China.