The electronic structure governs the reactivity of molecules and the properties of materials, making the accurate prediction and understanding of electronic structure critically important. Density Functional Theory (DFT) has emerged as the primary method for predicting material electronic structures; however, it has limitations when dealing with large-scale systems. In recent years, machine learning (ML) methods have been applied to learn electronic structures, but maintaining the precision of DFT for larger systems remains challenging. To overcome the limitation, This study has proposed a machine learning framework capable of efficiently and accurately predicting electronic structures across various scales. A team led by Prof. Attila Cangi from the Center for Advanced Systems Understanding and the Helmholtz-Zentrum Dresden-Rossendorf, Germany, has developed a machine learning approach with computational costs that scale linearly with system size, enabling efficient and accurate predictions of electronic structures at any scale. Unlike existing ML methods, this approach provides direct access to electronic structures and is not constrained by specific observable parameters. The study demonstrates that this method, applicable to systems where DFT is feasible, significantly accelerates computation speeds by up to three orders of magnitude, all while maintaining high computational accuracy. Most importantly, it enables electronic structure predictions at scales where traditional DFT calculations are unfeasible. This research successfully overcomes the computational bottleneck of DFT calculations for large-scale systems, offering crucial support for accelerating the discovery and optimization of new materials.