This paper presents the development and evaluation of a Sugeno Type-1 Fuzzy System for Heart Rate classification, integrating patient age and heart rate level as key inputs. Utilizing both trapezoidal and Gaussian membership functions, the study explores the effectiveness of these approaches in classifying risk levels. Experimental results from a dataset of 30 patients demonstrate that the trapezoidal model achieved a 100% accuracy rate, while the Gaussian model attained 93.33% accuracy, highlighting the potential for improvements in the latter due to instances of unclassified patients. The findings underscore the advantages of fuzzy systems in medical applications, emphasizing the need for refining membership functions and exploring Type-2 fuzzy systems to enhance performance in managing uncertainties. This work lays the groundwork for future research to expand the system’s applicability and robustness in real-time clinical settings.