Aim Effective implementation of the Global Biodiversity Framework and Global Strategy for Plant Conservation relies on accurate species distribution data. However, current vascular plant distribution data are often sparse and biased, requiring significant expansion. This study develops a scalable framework to prioritize areas for plant occurrence data acquisition, enabling botanical institutions to coordinate efforts and allocate resources efficiently.Location Global and regional.Methods Using a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) analysis, we prioritized areas based on: (a) ecosystem service value; (b) floristic value threatened by climate or land-use change; and (c) uncertainty in species richness estimates. Analyses were stratified into tree-dominated and grassland/deforested biomes. Prioritization maps were generated globally and for five major regions, then validated by field experts to ensure ecological relevance.Results The data-driven priority maps achieved high consensus among field experts, and high similarity between global and regional outputs demonstrated the model's scalability. By explicitly modelling sampling bias rather than relying on raw occurrence data, our framework successfully identified globally critical but historically overlooked areas for future data acquisition, such as Amazonia, the Putorana Plateau and Canada's Hudson Plain.Main Conclusions Our approach provides a transparent, adaptable tool for supporting national implementation of the Global Biodiversity Framework. A key advantage of this framework is its capacity to systematically translate expert opinion into explicit, quantitative criteria, which facilitates clear communication with policymakers and funders. By prioritizing data acquisition through tailored variables and weightings, this research promotes the efficient use of limited resources to address global biodiversity knowledge gaps.