The magnetotelluric (MT) inversion technology is crucial for quantitatively interpreting deep mineral resources, especially when combined with rock physics information, enhancing accuracy in assessing underground structural parameters and spatial distribution. However, the traditional fuzzy c-means (FCMs) clustering-constrained inversion method requires prior rock physics information for each geological unit, limiting their application scope. We propose a guided FCMs (GFCMs) clustering-constrained inversion method based on adaptive virtual rock physics information, referred to as XG-FCM-constrained MT inversion. This method breaks free from the constraints of traditional methods by not relying on prior rock physics information. In terms of extracting virtual rock physics information, we employ a local density clustering algorithm to dynamically extract resistivity model information from MT inversion iterations, automatically determining the number of clusters and cluster centers. Regarding the inversion strategy, we construct an integrated objective function that combines data fitting, smoothing constraint, and GFCM constraint, implementing a two-stage iterative solution strategy of “smoothing first, clustering second.” Model testing demonstrates that compared to traditional smoothing-constrained MT inversion, the XG-FCM-constrained method achieves a significant improvement in the resolution of resistivity model reconstruction, clearly delineating the boundaries of underground anomalies. Even in situations where rock physics information is insufficient or absent, this method can effectively reconstruct high-quality underground resistivity models, reducing the dependence on complete prior information. The application of actual field data further highlights the advantages of the XG-FCM-constrained MT inversion method, providing robust support for accurately delineating geological unit boundaries and precisely identifying potential ore deposit target areas.