Climate change and its associated environmental challenges pose significant threats to food security, particularly in arid and semi-arid regions, such as Iraq. This study employed an integrated geoinformation approach to assess the spatiotemporal impact of key environmental stressors on agricultural productivity over the past two decades (2003-2023). The primary objective of this study was to evaluate the influence of climate variability, land degradation, and water availability on food security in Iraq. Specifically, it aims to analyse changes in land use and land cover (LULC), land surface temperature (LST), vegetation health using the Normalised Difference Vegetation Index (NDVI), drought conditions using the Palmer Drought Severity Index (PDSI), soil moisture, soil pH, and demographic trends. A geospatial analysis integrating remote sensing and Geographic Information System (GIS) techniques (in short, Geoinformation) was conducted to identify environmental changes. Satellite-derived indices, such as the Normalized Difference Salinity Index (NDSI), Normalized Difference Turbidity Index, and Normalized Difference Tillage Index (NDTI), have been used to assess soil degradation and water quality. The findings revealed a significant increase in LST, with peak temperatures rising from 46.6 degrees C in 2003 to 49.9 degrees C in 2023, exacerbating drought conditions and reducing agricultural viability. Soil salinity, measured using the NDSI, indicated an upward trend, reaching a peak value of 0.52 in 2013, indicating worsening soil degradation. Water quality deteriorated, as reflected by rising turbidity levels (NDTI values peaked at 0.49 in 2008), affecting irrigation suitability. NDVI values declined from 0.41 in 2018 but showed partial recovery to 0.59 by 2023, suggesting the impact of land management efforts. This study identified high-risk zones where compounded environmental stressors threaten the food security. These results underscore the effectiveness of geo-information approaches in assessing the climate impacts on agriculture and offer a scientific foundation for policymakers to develop targeted mitigation strategies. Future research should explore machine learning models for predictive analyses and region-specific adaptation measures to enhance the resilience of agriculture.