To overcome this interpretive challenge, we developed an AI-assisted data interpretation system employing a neural network-based machine learning (ML) model. This model predicts the concentrations of specific ions within the electrolyte based on EIS measurements. Figure illustrates the distribution of prediction errors and the corresponding confusion matrices for various ions. As depicted, 85% of the prediction errors fall below 20%, demonstrating strong predictive performance. The confusion matrices reveal a maximum misclassification rate of less than 5%, indicating high model accuracy in distinguishing between different ions. The performance of the ML model across different concentration levels is presented in Figure . For the 100 nM concentration range-equivalent to parts-per-billion (ppb) levels of ion concentration, typical for heavy metal ion (HMI) contamination in drinking water or body fluids-75% of the prediction errors are below 50%, with an average error of less than 10%. The higher error in this concentration range is primarily due to the contamination levels being comparable to or even lower than the intrinsic concentration of water ions (H + and OH -), which dominate the interfacial structure. Additionally, measurement errors are more pronounced in this range, as the potentiostat exhibits greater inaccuracies at higher impedance levels. In the 10 µM concentration range, corresponding to parts-per-million (ppm) levels-typical for wastewater disposal or industrial processes-the model's performance improves significantly. Here, 75% of the prediction errors are below 30%, with an average error of around 10%, reflecting better accuracy at mid-range concentrations. For the 10 mM concentration range, 75% of the errors remain below 50%, with the average error consistently around 10%. The relatively higher error at this concentration, compared to the mid-range, may be attributed to the reduced interface thickness at higher ion concentrations. As a result, bulk solution properties begin to dominate over interfacial effects in influencing the impedance response, leading to less precise predictions.