The global state of food production data scarcity is a critical issue that affects the ability to achieve sustainable development goals, particularly those related to food security and nutrition. Food production data, including crop, livestock, and aquaculture statistics, are essential for understanding food systems and informing policy decisions. However, the lack of reliable, regularly collected, accessible, and spatially detailed data limits the accuracy of food production assessments and hinders the implementation of effective interventions. This review assesses the current state of food production data scarcity, highlighting the challenges in data availability, quality, and accessibility across different regions and food sectors. Crop production data is often inconsistent and incomplete, with significant variations in data quality and granularity across countries. While some countries, such as the USA, Brazil, India, and Australia, collect and report data at fine spatial scales, many countries only have data at coarse administrative levels. This limits the ability to accurately assess crop production patterns and trends, especially in regions dominated by smallholder farming. Efforts to improve data collection include the use of remote sensing, modeling, and survey-based data, but these methods face challenges in terms of cost, accuracy, and spatial resolution. Livestock production data is also limited, with many countries lacking comprehensive and up-to-date data on animal populations and production. While FAOSTAT provides valuable data on livestock production, it often masks subnational heterogeneity and local changes. The Gridded Livestock of the World (GLW) dataset provides a global standard for livestock population mapping, but data quality and resolution vary significantly across countries. Other global datasets, such as the Global Livestock Production Systems (GLPS) and the Herrero et al. dataset, offer insights into livestock biomass use and greenhouse gas emissions but face challenges in data harmonization and consistency. Aquatic food production data is similarly limited, with significant gaps in data availability and quality for both capture fisheries and aquaculture. FAO data on fisheries and aquaculture is often based on estimates and lacks detailed spatial and temporal information. The use of remote sensing and electronic monitoring has shown promise in improving data collection, but these methods face challenges in terms of data quality, storage, and transmission. Additionally, there is a lack of standardized definitions and reporting methods for aquatic food production, which complicates data comparison and analysis. Technical, institutional, and policy challenges hinder the collection, dissemination, and use of food production data globally. These challenges include inconsistent data collection practices, limited funding for data infrastructure, and policy silos that prevent data sharing and integration. Addressing these challenges requires coordinated efforts to improve data collection methods, enhance data governance, and ensure long-term funding for data systems. The integration of traditional and emerging data-gathering techniques, along with coordinated governance and dedicated long-term financing, is essential for overcoming current obstacles to sustained, up-to-date, and accurate food production data collection.The global state of food production data scarcity is a critical issue that affects the ability to achieve sustainable development goals, particularly those related to food security and nutrition. Food production data, including crop, livestock, and aquaculture statistics, are essential for understanding food systems and informing policy decisions. However, the lack of reliable, regularly collected, accessible, and spatially detailed data limits the accuracy of food production assessments and hinders the implementation of effective interventions. This review assesses the current state of food production data scarcity, highlighting the challenges in data availability, quality, and accessibility across different regions and food sectors. Crop production data is often inconsistent and incomplete, with significant variations in data quality and granularity across countries. While some countries, such as the USA, Brazil, India, and Australia, collect and report data at fine spatial scales, many countries only have data at coarse administrative levels. This limits the ability to accurately assess crop production patterns and trends, especially in regions dominated by smallholder farming. Efforts to improve data collection include the use of remote sensing, modeling, and survey-based data, but these methods face challenges in terms of cost, accuracy, and spatial resolution. Livestock production data is also limited, with many countries lacking comprehensive and up-to-date data on animal populations and production. While FAOSTAT provides valuable data on livestock production, it often masks subnational heterogeneity and local changes. The Gridded Livestock of the World (GLW) dataset provides a global standard for livestock population mapping, but data quality and resolution vary significantly across countries. Other global datasets, such as the Global Livestock Production Systems (GLPS) and the Herrero et al. dataset, offer insights into livestock biomass use and greenhouse gas emissions but face challenges in data harmonization and consistency. Aquatic food production data is similarly limited, with significant gaps in data availability and quality for both capture fisheries and aquaculture. FAO data on fisheries and aquaculture is often based on estimates and lacks detailed spatial and temporal information. The use of remote sensing and electronic monitoring has shown promise in improving data collection, but these methods face challenges in terms of data quality, storage, and transmission. Additionally, there is a lack of standardized definitions and reporting methods for aquatic food production, which complicates data comparison and analysis. Technical, institutional, and policy challenges hinder the collection, dissemination, and use of food production data globally. These challenges include inconsistent data collection practices, limited funding for data infrastructure, and policy silos that prevent data sharing and integration. Addressing these challenges requires coordinated efforts to improve data collection methods, enhance data governance, and ensure long-term funding for data systems. The integration of traditional and emerging data-gathering techniques, along with coordinated governance and dedicated long-term financing, is essential for overcoming current obstacles to sustained, up-to-date, and accurate food production data collection.