Water is one of the most crucial aspects of one’s life and as the population is increasing drastically, the need for water is also increasing. However freshwater resources are scarce, and freshwater is the main component for irrigation. As the technology is growing rapidly the agriculture sector needs to use advanced irrigation systems. This study addresses the water control project by exploring the combination of Artificial Intelligence, Machine Learning, and the Internet of Things. Drip irrigation, a key generation in water conservation, ensures managed water release to plant roots. However, traditional methods fall short in optimizing water use, necessitating the utility of device studying for sensible irrigation. By the use of the Kaggle dataset Irrigation Scheduling.csv comprising soil moisture, temperature, altitude, etc. The observations of this paper employ four ML models—LR, Naive Bayes, SVM, and XGBoost. This model is evaluated based on accuracy scores; results exhibit the effectiveness of XGBoost which emerges as the top-performing model with a perfect accuracy of 0.9979. This comparative evaluation presents treasured insights, setting up a basis for the mixing of devices and gaining knowledge of enhancing irrigation structures. The research contributes to sustainable water useful resource control in agriculture, important for addressing the worldwide project of water shortage.