In this study, we propose a ghost imaging method capable of penetrating dynamic scattering media through a multi-polarization fusion mutual supervision network (MPFNet). The MPFNet effectively processes one-dimensional light intensity signals collected under both linear and circular polarization illumination. By employing a multi-branch fusion architecture, the network excels at extracting multi-scale features and capturing contextual information. Additionally, a multi-branch spatial-channel cross-attention module optimizes the fusion of multi-branch feature information between the encoder and the decoder. This synergistic fusion of reconstruction results from both polarization states yields reconstructed object images with significantly enhanced fidelity compared to ground truth. Moreover, leveraging the underlying physical model and utilizing the collected one-dimensional light intensity signal as the supervisory labels, our method obviates the need for pre-training, ensuring robust performance even in challenging, highly scattering environments. Extensive experiments conducted on free-space and underwater environments have demonstrated that the proposed method holds significant promise for advancing high-quality ghost imaging through dynamic scattering media.