As important functional materials, piezoelectrics are widely used in various fields. However, the deviation of their elastic constants results in erroneous designs during application processes. Accurate characterization of the elastic constant is crucial for the correct design of piezoelectric devices.In contrast to other measurement methods, electrical impedance spectroscopy can only be carried out using an impedance analyzer, and the elastic constants of piezoelectric materials can be obtained by the inversion of the impedance spectroscopy data.In traditional electrical impedance spectroscopy,the measured impedance spectra are coincided with the calculated impedance spectra to the best possible extent by constantly modifying the material parameters. However, the process requires many iterations, making it tedious and time consuming.This study developed a forward model that yields elastic constants from impedance spectra by harnessing an artificial neural network. Upon measuring the impedance spectrum, the elastic constant can be obtained by only one forward calculation.COMSOL and MATLAB co-simulation were used to generate the data-sets.The discard method was employed to avoid overfitting of the model, and Pytorch was used for implementation. The resonant frequency error was reduced from the initial 2.8% to 0.8% after training.The proposed technique affords a reliable theoretical and practical approach for accurately measuring the elastic constants of piezoelectric materials.