Mainly rain-fed, irrigation is boosting production in new fig plantations. However, irrigation strategies also present challenges in water conservation. This research evaluates the application of non-linear regression models to analyze data collected from a set of sensors, and manual measurements to predict the water requirements of a fig orchard. This work assesses and compares the performance of various classical machine learning algorithms and artificial neural networks using data collected during the application of two water treatments listed as Control and RDS. The data were first analyzed to determine the relationship between variables. Then, the GridSearchCV technique was used to identify the best hyperparameter values for each algorithm, and finally, different machine learning techniques are applied. Preliminary results indicate that the designed algorithms provide accurate predictions for the water requirements of the fig tree. Machine learning algorithms reach \(Adj\_R^2\) values ranging from \(0.87-0.96\) and \(0.76-0.93\) for Control and RDS treatments, respectively. Results using artificial neural networks ranged from \(0.84-0.87\) in both water treatments. This demonstrates a good fit of the precision models.