Satellite remote sensing technology provides higher-quality typhoon satellite cloud map data, which is a major means of determining the intensity levels of typhoons, and this technology has been widely applied in typhoon forecasting. To address the problems of selective loss of cloud features in the oblivion gate and the loss of edge information caused by the fuzzy original truncation operation of the physical prediction results, this study proposes a typhoon class prediction method based on physical constraints and cloud map generation (CPGANTyphoon). The proposed method uses convolutional networks to approximate the physical equations, optimizes the feature extraction through prior knowledge, combines with adversarial training to improve image quality, uses a joint loss function to reduce visual disparities, and finally predicts typhoon levels for the generated images. Experimental results show that the CPGANTyphoon model generates the predicted images with a structural similarity index measure score of 0.916, a peak signal-to-noise ratio (PSNR) score of 30.36, a fuzzy c-mean accuracy of 0.981, and an overall accuracy of 0.985 for typhoon level prediction. The model can accurately generate typhoon cloud maps and predict typhoon levels for future moments.