In recent years, the use of Artificial Intelligence (AI) and Machine Learning (ML) approaches has demonstrated remarkable potential to improve crop output in agricultural practices, notably irrigation management. This review article seeks to aggregate and analyze advances in intelligent farming strategies that use AI and ML to optimize irrigation scheduling and water resource management in order to obtain improved agricultural yields. A thorough examination of significant research articles is offered, emphasizing the major contributions and insights from each study. The evaluated literature covers a wide range of topics related to intelligent irrigation systems, such as the use of wireless sensor networks, Internet of Things (IoT) devices, fuzzy systems, remote sensing, and mathematical models. The incorporation of AI and ML algorithms in these systems allows for real-time monitoring, data-driven decision-making, and predictive analytics. Adaptable irrigation techniques. Furthermore, the possibility of energy-efficient practices and resource sustainability is investigated. The findings of the literature study add to a thorough knowledge of the synergistic link between AI, ML, and agricultural practices, opening the way for the deployment of intelligent farming systems capable of dramatically increasing crop yields while conserving water resources.