Crop water requirement, the water a crop needs for optimal growth and yield throughout its growing cycle, including transpiration, evaporation, and other losses, must be accurately determined for irrigation scheduling, water resources management, and environmental analysis. Traditionally, this is performed using methods that depend on detailed climate data. However, in many areas, this data may not be available, and the process can take a lot of time. In such cases, using models to predict crop water needs is a good alternative. Machine Learning (ML), a kind of Artificial Intelligence (AI), offers tools that can learn from existing data and make future predictions. This study aimed to predict the water requirement of maize crop of the Samastipur district of Bihar, India, using ML models like Random Forest (RF), Multivariate Adaptive Regression Splines (MARSs), and Support Vector Machine (SVM). It used 20 years (2001–2020) of daily weather data, including maximum and minimum temperature, humidity, wind speed, and solar radiation. The water requirement was first calculated using the FAO-56 Penman–Monteith method combined with crop coefficients. The Gamma test helped choose the best input variables. The data was split into 80% for training the models and 20% for testing. To evaluate the models, this study used three performance measures: the Coefficient of Determination (R²), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE). Results showed that choosing the right model reduces errors and improves prediction accuracy. Among the models tested, Random Forest performed the best in both training and testing, followed by MARS and then SVM. These results highlight how effective ML models can be for accurately predicting crop water needs.