Syntheticaperture radar (SAR) and optical (OPT) image fusion aims to leverage their complementary information to obtain a more comprehensive representation of ground objects. However, significant discrepancies exist between the two modalities in terms of imaging mechanisms and feature distributions. Consequently, existing multi-modal image fusion methods struggle to achieve robust cross-modal feature alignment and deep semantic consistency between the fused results and the source modalities. To address the above challenges, this paper proposes a correlation and semantic prior-guided multi-scale cross-modal interaction network (CSP-MCIN) for effective SAR-OPT image fusion. Specifically, CSP-MCIN first employs two modality-specific encoders based on ResNet-18 to extract low-level details and high-level semantic features from SAR and OPT images, respectively. Subsequently, a multi-scale interactive decoder integrating cross-modal Transformers and gated fusion units is constructed to align and aggregate semantic and detail information from both encoders. Finally, to strengthen source-modality representations, a novel loss function combining a pixel-domain correlation loss and a CLIP-guided semantic consistency loss is designed and optimized under a PCGrad-based multi-objective optimization scheme. Experimental results on public SAR-OPT image datasets demonstrate that the proposed CSP-MCIN achieves superior fusion performance and computational efficiency compared with state-of-the-art approaches.