Predicting the response parameters of ice-covered overhead transmission lines (OHTLs) is critical for assessing the reliability of power systems. However, current methodologies face significant challenges: physics-based simulations are computationally demanding and thus infeasible for real-time applications, while conventional machine learning (ML) models often lack generalization capability due to the absence of physical constraints. To address these challenges, this article proposes a novel ph