Ionic liquids (ILs) exhibit fascinating properties due to special Z-bonds and have been widely used in electrochemical systems. The local Z-bond networks potentially cause a discrepancy in electrochemical properties. Understanding the correlations between the Z-bond energy (EZ-bond) and the electrochemical properties is helpful to identify appropriate ILs. It is difficult to estimate the correlations from single density functional theory calculations or molecular dynamic simulations. In this work, a machine learning model targeting the electronic density (ρBCP) of Z-bonds has been trained successfully, as expected for use in systems above the nanoscale size. The connection between the EZ-bond and the electrochemical potential window in ILs@TiO2, as well as that between the EZ-bond and the charge carrier mobility in ILs-PEDOT:Tos@SiO2, was separately investigated. This study highlights an efficient model for predicting ρBCP in nanoscale systems and anticipates exploring the connection between Z-bonds and the electrochemical properties of IL-based systems.