The goal of pansharpening is to restore the missing high-frequency details in the low-resolution multispectral (LRMS) image to generate its high-resolution multispectral (HRMS) counterpart by exploiting the high-resolution panchromatic (PAN) image as guidance. Previous research has predominantly focused on improving pansharpening performance for single satellites, often neglecting the challenge of generalization. Moreover, pansharpening is inherently an ill-posed problem. Precise and generalizable prior guidance is crucial for effectively addressing this issue. To this end, we propose conditional flow-based learning guided by implicit high-frequency priors (CFLIHPs) toward generalizable pansharpening. Specifically, we utilize implicit neural representation (INR) to precisely align implicit high-frequency texture priors from LRMS and PAN images within Fourier and gradient domains. The flow-based restoration module then leverages these priors as the guiding condition to restore domain-irrelevant high-frequency details, thereby facilitating effective cross-satellite generalization. Furthermore, to tackle the complex degradation process in real-world scenarios, we introduce noise perturbation to the high-frequency learning part, enhancing generalizability across diverse spatial resolutions and improving the robustness of our framework. Extensive experiments conducted on multiple satellite datasets demonstrate that our proposed framework outperforms state-of-the-art (SOTA) methods, achieving superior performance and excellent generalization results in both cross-satellite scenarios and full-resolution scenes.