Irrigation management remains one of the most important tasks in the agricultural sector, which is already suffering from a great deal of water loss through runoff and transport of pollutants that seriously threaten sustainability and crop yields. This paper provides a focused summary of recent research in the past five years on the applications of artificial Intelligence in precision agriculture, particularly in precision irrigation. It focuses on some key AI techniques such as machine learning, with particular focus on Support Vector Machines and Random Forest, advanced models of deep learning, including Long Short-Term Memory networks, and Reinforcement Learning. It thereby underlines these methods that are to be potentially useful in optimizing water application, soil moisture prediction, and irrigation scheduling. The results also indicate that the integration of artificial intelligence into agricultural practice contributes to minimizing water losses and promoting environmental sustainability for better crop yield. Challenges on scalability, data availability, and model accuracy are yet to be overcome under variable conditions. Future research should be hybrid approaches: using machine learning, deep learning, and Reinforcement Learning combined, together with different data sources like satellite imagery and IoT sensors to fine-tune these systems. By adopting artificial intelligence, agriculture can transcend traditional limitations, embracing innovation to ensure a sustainable future.