Interface plays crucial roles for mechanical and functional properties. Despite its importance, the structure-property relationships of the interface have not been fully understood because a determination of the interface structure accompanies huge computations which is caused by the degrees of geometrical freedom of the interface. The acceleration of the interface structure searching is important for the comprehensive understanding of the structure-property relationships of the interface. Here, we applied machine learning techniques for the interface structure searching. Two methods, virtual screening and kriging, are mainly introduced. Our methods achieved considerably high efficiency and can be applied to any materials.