Experimental validation using over 500 EIS measurements across diverse ion compositions and concentrations confirmed the model's predictive accuracy. Furthermore, the integration of machine learning algorithms enabled precise extraction of ion concentrations from impedance data, achieving high accuracy with reproducibility errors consistently below 3.5% over 250 days. This AI-assisted approach demonstrated remarkable performance in distinguishing between different ions and accurately predicting their concentrations across various ranges, from parts-per-billion to parts-per-thousand levels.