This article introduces a novel entropy measure for interval-valued fuzzy sets, expressing the entropy value as an interval rather than a single crisp value. Unlike conventional entropy measures, our approach accounts for uncertainty more comprehensively by incorporating multiple scenarios based on the relative positioning of intervals. We have also established the relationship of the proposed entropy measure with similarity and dissimilarity measures in the interval fuzzy environment. The proposed interval-valued entropy model enhances the scope of quantified assessment of interval uncertainty, making it more adaptable to real-world problems where precise entropy estimation is often challenging. The effectiveness of the proposed method is analyzed, highlighting its potential in decision-making and data analysis involving interval uncertainty.