Mesoscale air-sea interaction, which is active in Western Boundary Currents (WBCs), has a non-negligible effect on mid-latitude climate variability. The analysis and prediction of the mesoscale air-sea interaction rely on high-resolution observation datasets and mesoscale-resolving climate models, which often require long processing times to estimate future changes and have several limitations. Therefore, in this study, we used a newly developed iTransformer model, which integrates mesoscale sea surface temperature anomaly (SSTa) and latent heat flux anomaly (LHFa) coupling coefficient data to predict future changes in SSTa-LHFa coupling. First, we individually trained the model using data corresponding to 1-15 past winters from ERA5 dataset. Thereafter, we used the trained model to predict SSTa-LHFa coupling coefficient for the next 10 winters. Compared with the predictions using only the coupling coefficient, the prediction yields 3.0% relative improvements when SST data were incorporated. The iTransformer model also showed the ability to reproduce the linear trend and mean value of mesoscale SSTa-LHFa coupling coefficients. Furthermore, we chose the optimal input length for each WBC and used the model to predict changes in mesoscale SSTa-LHFa coupling in the future. The results thus obtained were comparable to those obtained using mesoscale-resolving climate models, indicating that the iTransformer model showed satisfactory prediction performance. Therefore, it provides a novel pathway for exploring mesoscale air-sea interaction variations and predicting future climate change.