Water resources are a core element of agricultural production, with their utilization efficiency directly influencing crop growth and yield. However, as global climate change and population growth intensify, water scarcity has become increasingly severe, necessitating a scientifically effective management strategy to optimize water resource allocation. Against this backdrop, big data and artificial intelligence (AI) technologies offer innovative solutions for analyzing and optimizing the relationship between water resource utilization and crop yield. This study systematically examines the correlation between water usage and crop yield by integrating historical, climate, and crop yield data to develop predictive models based on machine learning and deep learning. The research further explores the impacts of different irrigation methods, crop varieties, and environmental conditions on water use efficiency. It applies AI technologies to design optimized water management strategies aimed at maximizing crop yields. The research demonstrates these technologies’ successful application and effectiveness in regional water resource management through case studies, providing technical support and scientific evidence for optimizing agricultural water strategies and increasing crop yields. The findings offer new pathways and methodologies for achieving sustainable agricultural development under limited water resource conditions.