Soil conservation is crucial for sustainable agriculture, environmental stability, and long-term food security. Artificial intelligence (AI) techniques employs advanced approaches that are revolutionizing soil conservation through predictive analytics, real-time monitoring, and data-driven decision-making. Artificial intelligence methodologies, including (i) machine learning and (ii) deep learning, provide accurate evaluation of soil characteristics, prompt identification of degradation, and simulation of conservation solutions. These models promote erosion control, fertility management, and regenerative agriculture by offering evidence-based insights that enhance productivity and environmental sustainability. Predictive frameworks support adaptive land use planning, efficient resource allocation, and proactive responses to climate-induced soil challenges. In addition, AI-driven applications in soil microbiome modelling and bioinformatics contribute to innovative restoration strategies. Despite challenges such as limited datasets, system complexity, and cost constraints, advances in algorithms, computational power, and satellite data accessibility are expanding the scalability of these methods for both smallholder and large-scale farming systems. Future research could be focus on integrating AI-based modelling with traditional conservation practices to develop adaptive, cost-effective, and widely deployable solutions. This chapter emphasizes the vital role of AI-driven modelling in preserving soil health for future generations and promoting sustainable agriculture.