The realization of Cyber-Physical Systems (CPS) signals the potential resolution of various societal challenges. The exchange of diverse information between the physical and virtual realms necessitates the use of wireless systems, including those beyond 5G. In particular, the application of wireless emulators for the verification of wireless systems in virtual spaces is anticipated, enabling the efficient validation of systems with numerous wireless devices by replicating real-world communication environments in virtual spaces. However, accurate wireless communication emulation requires site-specific and high-precision radio wave propagation models. Recently, machine learning-based approaches have been proposed for modeling site-specific radio wave propagation characteristics. However, conventional models typically use training and test data from the same region, which can lead to degraded estimation accuracy when applied to unknown regions. This paper proposes a high-generalization model that improves estimation accuracy in unknown regions by using propagation path information as input features, based on the physical propagation mechanisms. The proposed model estimates a propagation path using machine learning and further uses this estimated path as input for machine learning to estimate propagation loss. When evaluating the estimation accuracy of propagation loss in unknown regions different from the training region, the proposed model reduced the RMSE from 5.52 dB to 4.69 dB compared to the conventional model, achieving an estimated error improvement of approximately 15%.