This study investigates the predictive performance of five models: Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Multivariate Adaptive Regression Splines (MARS), Piecewise Linear Regression (PLR), and Support Vector Regression (SVR) in estimating wind drift and evaporation losses (WDEL) in varying environmental conditions. Using a diverse dataset encompassing key environmental variables, such as riser height, operating pressure, nozzle diameters, wind speed, air temperature, and relative humidity, the models were trained and tested to assess their ability to capture complex, nonlinear relationships affecting WDEL. The results reveal that ANN outperformed all the other models, achieving the highest correlation coefficient values and the lowest root mean square error and mean absolute error, highlighting its superior ability to generalize to unseen data. In contrast, ANFIS and MARS exhibited moderate success, with higher prediction errors, especially in extreme conditions. PLR and SVR, which assume linear relationships, struggled to model the nonlinear dynamics governing WDEL, resulting in significantly lower accuracy. These findings underscore the importance of employing nonlinear models, such as ANN, to accurately predict WDEL in complex environmental systems. The study concludes that ANN is the most robust and reliable model for WDEL prediction, offering insights into future research directions, including hybrid models and ensemble approaches to further enhance predictive accuracy.