The topographic factor significantly influences the 3D inversion results of magnetotelluric data. Despite extensive research results previously obtained in suppressing topographic effects, the gridding of complex terrains (with significant elevation changes) is still challenged by grid design complexity and difficulty in correcting data elevation points. Based on the mainstream 3D inversion module ModEM for magnetotelluric data, this study proposed a novel method for rapid automatic grid design and partitioning of terrains based on unsupervised learning, primarily involving the K-means++ algorithm and the assessment of clustering effects. Compared to the uniform and equal proportion-based hierarchical methods ignoring the topographic factor, the proposed method shows the following advantages: (1) The terrain grid generated by the clustering-based hierarchical method manifested higher terrain approximation, reducing the average error between the terrain grid and the actual terrain by 25%; (2) The matching calculation for terrain correction based on the digital elevation model was somewhat avoided; (3) The rapid design of terrain grids can be achieved, and the hierarchical characteristics can be referenced for gridding in other modeling software. The proposed method was employed to demonstrate the whole process of partitioning the elevation data of a complex terrain in a mining area, generating a resistivity structure model more representative of the actual terrain characteristics. Based on this model, finer-scale 3D inversion results were obtained. Theoretical and practical applications illustrate that the proposed method can significantly improve the topographic adaptability of gridding, holding critical significance for suppressing topographic effects on the 3D inversion of magnetotelluric data.