This study introduces a novel multi-sensor system and a fusion strategy for continuous ice thickness detection. To address the limitations of traditional labor-intensive methods, which have lower resolution and are affected by environmental dependencies, we developed an enhanced air-coupled Ground Penetrating Radar (GPR) system integrated with temperature sensors and a transformer-based algorithm for anomaly compensation. It offers a reliable method for mapping ice structures by leveraging the dielectric contrast between ice and water. However, real-time monitoring during rapid temperature fluctuations remains challenging. The proposed method leverages Dynamic Time Warping (DTW) to identify significant signal changes, while the transformer network offers a physical information-supported prediction for ice thickness using environmental parameters. Results demonstrate that the proposed method improves detection accuracy, providing early warning capabilities and enhancing ice thickness tracking in dynamic conditions. The system real-time performance is validated through extensive field data from the Yellow River, showcasing the effectiveness of this integrative framework in challenging monitoring scenarios.