Provided is an impedance spectroscopy analytical method for concrete using machine learning. The method comprises identifying electrical flow through moisture and a conductive ion present in concrete using a node that measures electricity based on electrochemical impedance spectroscopy (EIS); generating a theoretical equivalent circuit model comprising a conductive path reflecting the electrical flow: normalizing an equivalent circuit reflecting a concrete microstructure based on an impedance experiment using the theoretical equivalent circuit model; and generating a predictive model for estimating a water and cement ratio from a parameter value of the equivalent circuit through machine learning. Accordingly, the accuracy and reliability of estimating the microstructure and mixing ratio of the cement-based material can be increased.