Soil mapping, an essential aspect of environmental management, agriculture, and land-use planning, has advanced significantly with the adoption of Remote Sensing (RS) and Geographic Information Systems (GIS). Traditionally dependent on labour-intensive ground surveys, modern soil mapping now utilizes satellite and airborne sensors to effectively evaluate soil characteristics like texture, moisture content, and salinity across extensive areas through remote sensing processes. Techniques such as spectral reflectance analysis and SAR interferometry, combined with GIS's ability to integrate and analyse spatial data, have made soil mapping more accurate and comprehensive. Despite challenges like resolution constraints and data availability, advancements in technologies such as hyperspectral imaging, UAVs, IoT, and machine learning offer substantial potential to improve the precision and predictive capabilities of soil mapping. Global collaboration and the incorporation of climate models further emphasize the promise of these innovations in addressing environmental challenges, promoting sustainable land management, and enhancing the understanding of soil resources.