To address this gap, a comprehensive theoretical model of EIS must account for both the static (DC) properties of the interface, such as EDL capacitance, and the dynamic (AC) behaviors, including non-ideal impedance phenomena like the constant phase element (CPE). Connecting these aspects is crucial for understanding the complex interplay between the structural properties of the interface and the observed impedance responses. Such insights would enable more effective applications of EIS in practical contexts, including the development of advanced sensors. Addressing this divide will align foundational research with applied methodologies, driving innovation in both scientific inquiry and real-world applications. This paper aims to address this challenge by linking the structural characteristics of the electrode/electrolyte interface to the resulting impedance responses induced by changes in electrolyte composition. Our primary focus is on developing a continuumbased model of the electrode/electrolyte interface that incorporates fundamental ion properties-such as size, mobility, and adsorption-drawing on recent advancements in the understanding of EDL structure to explain these relationships. To validate this model, we conducted extensive experimental studies using over 500 aqueous solutions as a proof of concept, demonstrating its applicability for detecting and quantifying electrolyte components via EIS. Our experimental results exhibit strong alignment with model predictions, confirming the significant influence of ionspecific characteristics on impedance responses. To further address the complexity inherent in these relationships, we employed machine learning algorithms to analyze the experimental data, providing deeper insights and enabling more accurate predictions. This integrated approach demonstrates significant potential for enhancing the precision and efficiency of electrolyte component detection, representing a notable advancement in electrochemical analytical methodologies.