This research explores the use of a WO₃/Co-ZIF nanocomposite for the removal of cefixime from aqueous solutions, employing various machine learning models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). The primary goal is to optimize the conditions for cefixime degradation with high accuracy. The quadratic factorial model in RSM was selected as the best model based on R analysis, and it identified pH, reaction time, and catalyst amount as the most significant factors. The SVR model was chosen as the best for prediction due to its higher R² Score (0.98) and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both models identified pH as the most important parameter. GA and SOLVER models were used to determine the optimal values for initial cefixime concentration, pH, time, and catalyst amount, which were (6.14 mg L⁻¹, 3.13, 117.65 min, and 0.19 g L⁻¹) and (5 mg L⁻¹, 3, 120 min, and 0.19 g L⁻¹), respectively. The study contributes to advancements in intelligent decision-making and optimization of pollutant removal processes, offering practical solutions for environmental remediation.This research explores the use of a WO₃/Co-ZIF nanocomposite for the removal of cefixime from aqueous solutions, employing various machine learning models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). The primary goal is to optimize the conditions for cefixime degradation with high accuracy. The quadratic factorial model in RSM was selected as the best model based on R analysis, and it identified pH, reaction time, and catalyst amount as the most significant factors. The SVR model was chosen as the best for prediction due to its higher R² Score (0.98) and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both models identified pH as the most important parameter. GA and SOLVER models were used to determine the optimal values for initial cefixime concentration, pH, time, and catalyst amount, which were (6.14 mg L⁻¹, 3.13, 117.65 min, and 0.19 g L⁻¹) and (5 mg L⁻¹, 3, 120 min, and 0.19 g L⁻¹), respectively. The study contributes to advancements in intelligent decision-making and optimization of pollutant removal processes, offering practical solutions for environmental remediation.