This study presents a new similarity measure for Intuitionistic fuzzy sets (IFS) to improve hierarchical clustering algorithm. Traditional similarity measures often incur high computational costs when generating associative matrices for cluster membership determination, particularly when dealing with large datasets. The methodology develops a mathematical framework for the novel similarity measure, which is applied in hierarchical clustering algorithm by calculating equivalent similarity matrices and comparing different datasets using the dendrogram. Extensive testing on synthetic and real-world datasets, including flood occurrence analysis and car selection problems, shows that the proposed method improves cluster separation and significantly reduces computational costs. The contributions of this work offer a more efficient and effective similarity measure, enhancing decision-making processes in various applications.