Alkaline deep eutectic solvents (DESs) have been widely employed across diverse fields. A comprehensive understanding of the alkalinity data is imperative for the comprehension of their performance. However, the current range of techniques for quantifying alkalinity is constrained. In this investigation, we formulated a series of alkaline DESs and assessed their basicity properties through a comprehensive methodology of Hammett functions alongside 1H NMR analysis. A correlation was established between the composition, structure and alkalinity of solvents. Furthermore, a strong linear correlation was observed between the Hammett basicity (H-) of solvents and initial CO2 adsorption rate. Machine learning techniques were employed to predict the significant impact of alkaline functional components on alkalinity levels and CO2 capture capacity. This study offers valuable insights into the design, synthesis and structure-function relationship of alkaline DESs.