Water scarcity and inefficient resource utilization continue to challenge global agriculture, threatening crop productivity and environmental sustainability. Precision agriculture, empowered by the synergy of Artificial Intelligence (AI) and the Internet of Things (IoT), offers a promising approach to optimize water and resource management. This research presents a comprehensive AI-IoT framework that integrates predictive machine learning models with real-time IoT sensor networks to monitor soil moisture, crop health, and environmental conditions across wheat, maize, and rice fields. Random Forest and Long Short-Term Memory (LSTM) algorithms are utilized to forecast irrigation requirements and guide resource allocation, while IoT devices enable automated data acquisition and irrigation control. Experimental deployment over a complete growing season demonstrates that the proposed system can reduce water consumption by approximately 40% and increase crop yield by 18% compared to conventional irrigation practices. Furthermore, the framework improves fertilizer and energy efficiency by 12–15%, highlighting its contribution to both economic viability and ecological sustainability. Challenges such as sensor calibration, data noise, and deployment costs were addressed to ensure reliability and scalability. The findings confirm that AI-IoT integration facilitates data-driven decision-making, precise irrigation scheduling, and resource-efficient farming. By providing a scalable and adaptable model, this study advances smart farming technologies and supports sustainable precision agriculture across diverse crops and climatic conditions. Overall, the research underscores the transformative potential of AI-IoT systems in enhancing productivity, conserving resources, and promoting environmentally responsible agricultural practices.

Keywords: Precision Agriculture, AI, IoT, Water Optimization, Resource Efficiency, Sustainable Farming, Smart Irrigation