As the overall water content increased, the membrane resistance decreased; however, after saturation, the membrane resistance ceased to decline. This phenomenon is reflected in the increase of the correlation coefficient between the water content and membrane resistance from the lowest point of -0.876 to -0.612. The anodic charge transfer resistance and cathodic double-layer capacitance exhibited a proportional relationship with overall water content, with correlation coefficients of 0.666 and 0.655 when using all samples. This strong correlation suggests a nonlinear relationship between impedance spectra and overall water content. Through the nonlinear fitting capability of an ANN model using real and imaginary parts of all impedance points as the input, the correlation coefficient for 75% of the training data reached 0.917. The ANN model demonstrated a comparable correlation coefficient of around 0.884, even for untrained test samples. With the AI-assisted technology, it is possible to establish a correlative relationship between a high-end diagnostic instrument in a national facility and a low-cost tool in the laboratory that offers a viable pathway to predict the water content without expensive tools in future studies. The potential to predict water content through EIS offers a promising way for developing more effective onboard water management, and could provide crucial theoretical support for subsequent research on fuel cell performance degradation, fault diagnosis and lifespan prediction to be conducted independently by neutron imaging equipments.