Soil quality is critical for productivity and environmental resilience, and it influences food security. Soil quality assessment has become crucial for monitoring changes in soil function, directing sustainable management approaches, and assisting in the early detection of possible degradation. Geoinformatics, encompassing Remote Sensing (RS) and Geographic Information Systems (GIS), has developed as an effective technique for evaluating and modeling soil quality. By capturing spatial variability in soil properties, geoinformatics facilitates the creation of precise and actionable datasets for effective land resource management. Recent advancements, including the integration of geospatial data with machine learning, have revolutionized soil quality assessments, enabling real-time monitoring and analysis of soil health. By leveraging spatially explicit data, this approach evaluates key physical, chemical, and biological soil quality indicators, across diverse landscapes and scales. Predictive modeling using geoinformatics enhances decision-making in precision agriculture, optimizes resource utilization, and supports environmental sustainability by highlighting regions at risk of degradation and those requiring targeted interventions. Ongoing developments in the field of sensors, data mining techniques, and artificial intelligence continue to broaden the accuracy and scope of geoinformatics for soil quality monitoring. This chapter focuses on geoinformatics-based modeling for soil quality control, emphasizing its importance in improving agricultural operations, protecting environmental integrity, and addressing emerging trends.