Ions play a fundamental role in solid-liquid interface processes, whether as essential or undesirable components, highlighting the need for precise and quantitative real-time monitoring. Electrochemical sensors are identified as promising tools, particularly for field-deployable applications. However, conventional electrochemical sensing is inherently restricted to redox-active species and is often single use, constraining its scope. This study presents electrochemical impedance spectroscopy as an alternative for ion detection, utilizing physico-chemical interactions at the electrode-electrolyte interface. We introduce a first-principles model that describes the interfacial impedance behavior and shows how ion specific processes shape the impedance response. Based on this framework, an extensive dataset is compiled, and a machine learning model is trained to predict electrolyte composition with consistent accuracy, demonstrating detection limits at the parts-per-billion level. The findings indicate that this method has considerable potential as a real-time method for ion sensing, providing a perspective on selectivity and sensitivity beyond traditional electrochemical approaches. This work could serve as a foundation for advanced models of impedance behavior, and development of impedance-based sensors with applicability in complex environments, including biological fluids and industrial liquids.