The increasing strain on global freshwater resources, exacerbated by climate variability and the rising demand for food, underscores the urgent need for sustainable agricultural practices. Traditional irrigation methods, often reliant on fixed schedules and manual oversight, contribute to inefficient water use and limited adaptability to dynamic environmental conditions. This study presents a comprehensive framework that integrates Artificial Intelligence (AI) and Internet of Things (IoT) technologies to address the limitations of conventional resource management in agriculture. Central to the proposed approach is a smart irrigation system that leverages real-time environmental data—such as soil moisture, weather forecasts, and crop-specific parameters—to deliver precise, adaptive recommendations for irrigation and input usage. The framework employs machine learning algorithms and cloud-based analytics to optimize resource allocation while ensuring scalability and user accessibility. Case studies conducted across diverse agro-climatic regions demonstrate significant improvements in water-use efficiency, reduced agrochemical consumption, and enhanced crop yield. These findings validate the potential of AI-driven systems to support resilient, data-informed agricultural practices that align with broader goals of environmental sustainability and food security.

Keywords: Smart Irrigation, Artificial Intelligence in Agriculture, Resource Optimization, Precision Farming, IoT-based Agriculture, Sustainable Crop Management