Global food trade increasingly drives national environmental footprints, yet accurate assessments are constrained by critical gaps in Life Cycle Inventory databases. Existing data underrepresent key regions such as Latin America, Africa, and Asia, and processing and transport stages are often excluded. Most studies focus narrowly on limited set of indicators like carbon footprints, overlooking broader trade-offs such as water scarcity or biodiversity that depend on local contexts. Current extrapolation methods attempt to address these gaps but are data-intensive and risk distorting original emission models, limiting their scalability. To overcome these challenges, we developed a standardized framework that uses multivariate similarity analysis of large agricultural databases to rapidly construct suitable proxies from existing Life Cycle Inventory (LCI) datasets.The framework integrates four pillars: proxy identification via hotspot parameter comparison (e.g., yield, fertilizer use), composite proxy construction using weighted averages of similar LCI datasets, a Data Quality Index (DQI) to assess uncertainty across 6 criteria, and a Principal Component Analysis (PCA) over 22 environmental impact categories to simplify interpretation without aggregation. This framework offers a rapid and scalable solution for addressing LCI data gaps by combining statistical analysis with established databases. It preserves the integrity of original emission models while enabling robust comparisons across regions. The DQI not only assesses uncertainty but also identifies critical data gaps, guiding future studies to prioritize regions and products where high-quality data are most urgently needed. By bridging these gaps, the framework supports transparent and evidence-based decision-making for sustainable global food systems.