<p>This paper proposes eight novel similarity measures for <i>T</i>-spherical fuzzy sets, addressing the limitations of existing measures in distinguishing closely related arguments. These new measures are specifically designed to enhance granularity and discriminative power, making them highly effective in complex decision-making scenarios such as pattern recognition and medical diagnosis. Additionally, Hamacher <i>t</i>-conorms and <i>t</i>-norms are introduced to aggregate <i>T</i>-spherical fuzzy numbers, offering a generalization of algebraic and Einstein classes of <i>t</i>-conorms and <i>t</i>-norms in aggregation theory. A numerical example demonstrates the effectiveness of the proposed similarity measures over other existing similarity measures, and a case study in medical diagnosis highlights their practical application. Comparative analysis with existing methods shows that the proposed approach not only overcomes the limitations of traditional similarity measures but also enhances the efficiency of pattern recognition in solving medical diagnosis problems. The study highlights its potential to improve performance in medical diagnosis and pattern recognition applications by offering a novel theoretical framework and validated practical tools.</p>