Accurate prediction of sea surface temperature (SST) is vital for marine fishery production and the prediction of marine dynamic environment information. The traditional numerical prediction methods have high calculation costs and low time efficiency. However, the existing data-driven SST prediction methods mainly target the single observation point and fail when it comes to a sea region composed of multiple observation points. The existing regional SST prediction methods still have a long way to go in prediction accuracy. Therefore, we propose a regional SST prediction method based on XGBoost and PredRNN++ (XGBoost-PredRNN++). The method firstly converts SST data into gray images and then extracts the time characteristics of each point by the XGBoost model. On this basis, the CNN network is utilized for fusing the time characteristics into the original SST data, and the spatial dependence is extracted at the same time. Finally, the latest time series prediction model PredRNN++ is adopted to extract the temporal and spatial correlations among SST data to achieve the high-precision prediction of regional SST. The experimental results show that the high prediction accuracy and efficiency of the proposed method are superior to those of the existing methods.