In this work, we present the development and application of an Interval Type-2 Fuzzy System designed to classify nocturnal blood pressure, a crucial factor in determining the risk of heart disease. Traditional methods often face difficulties due to the inherent uncertainties and variations in blood pressure readings during sleep, which can affect classification accuracy. The proposed system utilizes the robustness of interval type-2 fuzzy logic to handle these uncertainties more effectively and accurately. This study details the classifier’s architecture, including the selection and design of membership functions and the rule bases necessary for accurate classification. The system’s performance is evaluated using a dataset collected from 20 patients, demonstrating significant improvements in classification accuracy and reliability when compared to Type-1 Fuzzy Systems. The results suggest that the Interval Type-2 Fuzzy System demonstrated high accuracy, with the trapezoidal membership functions achieving 95% classification precision and only one misclassification. Similarly, Gaussian membership functions yielded an 89% accuracy with two misclassifications. These findings indicate that the system is a promising tool for cardiologists in monitoring and managing nocturnal hypertension, offering improved prediction and prevention of heart diseases.