Climate change and inefficient irrigation practices have underscored the need for sustainable water management in agriculture. This study focuses on fig trees (Ficus carica L.), a crop adapted to arid conditions, whose irrigation needs must be managed to optimize yield without overuse of water. Using a combination of artificial intelligence (AI) techniques - genetic algorithms (GA) and neural networks (NNs), this research proposes a cost-effective method to predict water requirements using sensor data and climatic variables. Experiments conducted at the Extremadura Scientific and Technological Research Center (CICYTEX) assessed various sensor configurations and combinations of sensors with climatic data. The study compared models using all available inputs versus reduced sets selected via GAs. The results indicate that the use of only three sensors achieves a near-optimal predictive accuracy (MSE = 0.0205) compared to the models using all sensors (MSE = 0.0135). Similarly, the integration of climatic variables showed the feasibility of achieving high accuracy with fewer data points. The results highlight the potential of hybrid AI models to minimize the reliance on sensors while maintaining prediction accuracy, thus optimizing irrigation to meet the specific needs of the crop. This approach represents a step toward sustainable agricultural practices by balancing resource efficiency and crop productivity.