<p>Distance measures are among the most prominent mathematical tools used to distinguish information in Pythagorean fuzzy set (PFS). Divergence measures serve as another such tool; however, limited work has been conducted on divergence measures within the PFS environment. In this article, two distance measures are introduced, both of which incorporate a newly proposed divergence measure. It has been observed that, in the existing PFS literature, distance measures have not been categorized into different types. Accordingly, this study categorizes distance measures into moderate, pessimistic, and optimistic viewpoints, based on varying perspective of information. This work establishes that in certain instances where a distance measure that adopts a moderate viewpoint fails to yield a conclusive result, a distance measure adopting either a pessimistic or an optimistic viewpoint can be conclusive. Found that most existing distance measures in PFS are based on a moderate viewpoint, there is a clear need for a measure that adopts an optimistic viewpoint. The rationality and advantages of the measures are demonstrated through numerical examples and comparative analysis. Furthermore, the applicability of the measures to real-life problems are established through various existing problems and a case study on Human Development Index report 2023/2024. The statistical inference highlights very high correlation with respect to ranking of alternatives which ensure that the measures yield similar results to that of the existing measures. Moreover, the sensitivity analysis proves the stability and reliability of ranking of alternatives in the Human Development Index problem. The other salient features like low variance and standard deviation for the applications exhibit the efficacy and out-performance of the proposed distance measures.</p>