Edge computing, an essential aspect of modern computing, is particularly well-suited for vessel trajectory prediction due to its ability to process vast amounts of real-time data, thereby overcoming the limitations of traditional centralized methods. Despite the existence of various prediction models, there is limited research focused on long-distance trajectory prediction. This study proposes a Transformer-GRU-based method to enhance the accuracy of long-distance vessel trajectory predictions. Historical AIS data for multiple vessels were collected, preprocessed using cubic spline interpolation, and validated through a combination of Transformer-GRU prediction and residual compensation. The proposed model outperforms seq2seq and GRU models, achieving mean squared error (MSE) values for longitude and latitude prediction errors below \(6\times {10}^{-5}\) for long-distance predictions, thereby providing a more accurate approach for long-distance vessel trajectory prediction. Comparatively, the Transformer-GRU model improves performance by 36.51% over the GRU-only model, albeit with a time cost increase of 41.69%.