Building upon prior research employing ML methods, we aim to develop an ML-model to predict the atom-specific electronic structure of all orbitals in their ground state for solid materials from ELNES/XANES, towards the realization of atomic-level analysis of ground-state electronic structures. In this study, we focus on the Si-K edge of Si. Si is an indispensable material in industrial applications despite its simplicity, and the Si-K edge, located at around 1,700 eV, is sufficiently localized to enable atomic resolution ELNES observations. We constructed a database of Si-K ELNES/XANES and the corresponding ground-state PDOS for various structures of Si using DFT simulations. Utilizing this database, we developed an ML model based on onedimensional neural network to predict the ground-state PDOS from the Si-K edge. Si-K edge involves the excitation of 1s core electron to p-orbitals of conduction band, thus providing the porbitals of conduction band at the excited state. We demonstrate that a ML model can capture the complex correlation between Si-K edge and the ground-state PDOS, thereby enabling the prediction of PDOS for not only transition-allowed p-orbitals but also transition-prohibited sorbitals, not only of conduction band but also of valence band. Additionally, by conducting a comparison with template matching, we demonstrate the utility of out ML based approach.