**Abstract:** This research introduces a novel approach to enhancing the sensitivity and accuracy of electrochemical sensors by integrating dynamic frequency-modulated impedance spectroscopy (DFMIS) with a machine learning (ML) predictive calibration model. Traditional impedance spectroscopy (IS) offers rich information about interfacial reactions, but its static nature limits responsiveness to rapidly changing analyte concentrations. DFMIS addresses this limitation by dynamically sweeping the frequency range during measurement, capturing transient interfacial behavior.