Cyclic voltammetry (CV) has been a powerful technique to provide impactful insights for electrochemical systems, including reaction mechanism, kinetics, diffusion coefficients, etc., in various fields of study, notably energy storage and energy conversion. However, the separation between the faradaic current component of CV and the nonfaradaic current contribution to extract useful information remains a major issue for researchers. Herein, we report a deep learning algorithm inspired by speech denoising that utilizes the theoretical faradaic current as a study target and predicts it from the overall current response from cyclic voltammograms. This deep neural network (DNN) is constructed from a series of fully connected layers, which apply a weight matrix to the inputs and transform it using an activation function to obtain the desired regression. Our model performed well with overall mean absolute percentage errors (MAPEs) of 6.36% between theoretical faradaic currents and the predicted responses from the total currents, with a peak position difference of 2.56 mV for anodic peaks and 2.44 mV for cathodic ones. Furthermore, the algorithm is also capable of extracting peak current values from experimental data with 3.37% MAPE and minimal peak position error (less than 0.75 mV). This innovative approach may be used as a tool to assist researchers in studying electrochemical systems using CV.