Soil water retention capacity is a critical factor influencing farmland productivity, the sustainability of agricultural ecosystems, and resource use efficiency. Due to significant variations in water retention performance among different soil types, optimizing soil improvement measures to enhance this capacity is very important. This study systematically analyzes the mechanisms of various soil improvement techniques, including the addition of organic materials such as biochar and compost, as well as the effects of water-retaining agents and novel materials on soil structure and water retention. By integrating machine learning methods, a model was developed to precisely evaluate the water retention effects of these improvement measures, along with a data-driven intelligent recommendation system that provides tailored solutions for different soil types and environmental conditions. The findings demonstrate that the application of artificial intelligence in soil management improves the efficiency and accuracy of improvement techniques and provides scientific support for sustainable agricultural production. This study offers practical insights for advancing innovations in soil improvement technologies and promoting intelligent agricultural management.