Soil conservation is the central pillar of sustainable agriculture. However, soils worldwide face increasing threats from erosion, nutrient depletion, salinization, and unsustainable farming practices. Traditional conservation methods such as terracing, mulching, and cover cropping often lack the precision required to address rapidly evolving agricultural and environmental conditions. Artificial Intelligence (AI) has been increasingly adopted as a powerful tool to advance soil conservation efforts. AI approaches utilize data from remote sensing, geographic information systems (GIS), unmanned aerial vehicles (UAVs), and in-field sensors to perform accurate modeling, prediction, and monitoring of soil conditions. Techniques including machine learning, deep learning, computer vision, and hybrid approaches have shown promising results in applications from soil erosion risk mapping and fertility prediction to automated quality assessment and decision support for conservation planning. AI not only improves the efficiency and adaptability of conservation practices but also enables the implementation of site-specific and effective strategies that are important for sustainable land management. This chapter provides a comprehensive overview of the methodologies, applications, and limitations of AI in soil conservation. Additionally, the chapter outlines research opportunities for future development.