Through the nonlinear fitting capability of ML, such as ANN, even for data that have not been previously trained, ANN can predict the overall water content based on impedance information (test samples illustrated in the Fig. and). After collecting neutron imaging and electrochemical impedance data for varying water content and training the ML model, the water content information from costly and intricate neutron imaging experiment can be linked to the EIS. In onboard systems, real-time water content estimation can thus be achieved by using simple, accessible impedance measurements when appropriate ML models have been constructed and pre-trained. This represents significant progress in the capability of simple electronic signals to perform advanced diagnostics in electrochemical systems. Estimated water content can be directly used as a feedback control parameter for water management strategies.