Soil degradation and desertification significantly threaten global agricultural sustainability, particularly in arid and semi-arid regions. Declining soil health, reduced water availability, and land productivity loss demand urgent and effective conservation strategies. In-situ soil and water conservation (SWC) techniques, including contour ploughing, terracing, mulching, cover cropping, agroforestry, rainwater harvesting, micro-irrigation, percolation pits, and trenches, provide viable solutions by enhancing water retention, reducing erosion, and improving soil fertility. However, widespread adoption is hindered by high implementation costs, the need for specialized knowledge, and long-term maintenance challenges. This chapter explores innovative SWC practices with a focus on integrating geospatial technologies and predictive modeling to assess soil degradation hotspots, optimize interventions, and monitor long-term impacts. Remote sensing and GIS-based spatial analysis offer valuable insights into soil moisture variability, erosion-prone areas, and land-use changes, facilitating precision conservation planning. Additionally, AI and machine learning (ML) advancements present opportunities to improve real-time SWC monitoring, predictive assessments, and decision-support systems tailored to diverse soil and water conditions. The objectives of this chapter include evaluating the effectiveness of SWC techniques through technological innovations, identifying priority areas for intervention, and recommending data-driven conservation strategies. Future research should refine geospatial models for real-time monitoring, integrate AI-driven predictions, and develop user-friendly decision-support tools for farmers and policymakers. By leveraging advanced technologies, this approach enhances resilience against soil degradation and desertification, ensuring sustainable agricultural productivity and long-term environmental conservation.