This research paper introduce Symmetrical and sequentially dense soft set and isometry in the context of POS topological space. Inspired by Matthew’s seminal work on dataflow network research, we extend the study to soft universe, bridging classical metrics with soft computing to represent imprecision in complex systems. We investigate sequence convergence in partial soft topological spaces, revealing essential insights and contributing to our understanding of partial soft metrics. Additionally, we introduce the idea of Convergence of partially ordered soft topological spaces along with we provide examples to support the obtained results and relations. In the future, these results can be further applied to analyze and understand the properties and convergence of soft topological spaces with the contraction condition for soft fixed point theory.