Efficient and responsive traffic engineering is crucial for maintaining the robustness and reliability of Wide Area Networks (WANs). Traditional traffic engineering approaches often struggle to adapt to the dynamic and complex demands of today's network environments. To address these challenges, this paper enhances the Traffic Engineering algorithms by integrating an attention mechanism within the Edge-Path Embedding component. This significantly improves the model's adaptability and decision-making accuracy. Our comprehensive experimental evaluations demonstrate substantial improvements in terms of satisfied traffic demands and computational efficiency, highlighting the effectiveness of our approach.