Effective policy implementation requires a comprehensive understanding of the prevailing conditions within the administrative regions where the policy is deployed, as well as a clear evaluation of its impact on the corresponding Sustainable Development Goals (SDGs). To support this, data systems must be equipped to identify, integrate, and analyze data points specific to each administrative level, including states, districts, and taluks, along with indicators associated with the relevant SDGs. However, existing data systems often fail to establish these linkages effectively, limiting their ability to support integrated policy analysis and decision-making. This research presents Sadrusha, an innovative decision-analytical system developed to address critical gaps in policy evaluation and data integration. Derived from the Sanskrit word meaning “affinity” or “similar behavior”. The proposed approach integrates open government datasets, geospatial shapefiles, and policy documents to construct an Administrative Data Twin—a sophisticated data model that links Sustainable Development Goals (SDGs) with geographic and temporal dimensions. By automatically identifying and contextualizing relevant data based on SDG targets, administrative boundaries, and timeframes, Sadrusha enables granular, cross-domain policy analysis. Utilizing five years of data from the “Karnataka at a Glance” (KAG) repository, the system has been validated against 40 policy documents to assess its effectiveness in supporting dynamic policy evaluation. The proposed framework offers a scalable, data-driven approach for enhancing policy coherence and generating actionable insights aligned with the SDGs.