The T-spherical fuzzy hypersoft set (TSFHSS) is a generalization of a spherical fuzzy soft set, designed to handle uncertainty and complex data in real-life decision-making scenarios. Due to its unrestricted uncertainty component, TSFHSS offers more adaptability to decision-makers in achieving optimal solutions. First, this manuscript introduces some similarity measures (SMs) within the TSFHSS framework, including the cosine and set-theoretic similarity measures, along with their weighted versions. The theoretical properties of these measures are explored and validated through mathematical proofs. Furthermore, a decision-making algorithm utilizing the proposed similarity measures is developed. To illustrate its effectiveness, a computational example is presented in the context of medical diagnoses as a pattern recognition application. The results highlight the potential of TSFHSS in improving decision accuracy in complex, uncertain, and overlapping environments.