A novel theoretical decision support system upon compensatory fuzzy logic is presented. The system’s objective is the evaluation of cardiovascular risk through Arterial Diameter Variation signals. This system is designed as a foundational framework for the development of clinical tools that are oriented towards user requirements. A proprietary dataset, derived from pulse wave analysis of 829 individuals exhibiting both normotensive and hypertensive conditions, was employed to extract signal indices, including the Radial Augmentation Index, Diastolic Augmentation Index, and the Propagation Speed of the Arterial Pressure Wave. These indices were subjected to statistical and exploratory analysis to facilitate the development of fuzzy inference rules. Expert-defined membership functions and a rule base were constructed to enable the fuzzy characterization of the indices. Subsequently, a fuzzy inference system was proposed for the evaluation of cardiovascular risk. This decision-making system incorporates dual-implication rules and compensatory logic to address physiological variability and enhance risk assessment. The primary objective of this preliminary work is to augment clinical decision-making by formally integrating expert knowledge into the assessment of arterial health and the prediction of cardiovascular disease risk, including hypertension. This approach facilitates data interpretation under conditions of uncertainty, accounting for inter-individual variability and the inherent imprecision of arterial signal indices. Discernible patterns of arterial health were identified, providing robust tools for disease prevention and enabling clinicians to tailor evaluations to individual patient profiles. The results demonstrate the system’s potential for the early detection of arterial dysfunction and its applicability in personalized cardiovascular risk monitoring. Validation with clinical experts affirmed the system’s interpretability and potential clinical utility.