This study investigates the removal of cefixime from aqueous solutions using a WO₃/Co-ZIF nanocomposite and employs machine learning methods for modeling and optimization. The research aims to enhance the efficiency of cefixime degradation through intelligent decision-making and process optimization. Various models, including Support Vector Regression (SVR), Artificial Neural Network (ANN), Response Surface Methodology (RSM), Genetic Algorithm (GA), and Solver, were used to predict and optimize the removal process. The quadratic factorial model in RSM was selected as the best model for predicting results, with regression coefficients used to evaluate artificial intelligence models. The SVR model was found to be the best for prediction, with a high R² score (0.98) and low MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter. GA and Solver models were used to determine optimal conditions for cefixime removal, yielding optimal values of 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. The study highlights the effectiveness of the WO₃/Co-ZIF nanocomposite in cefixime removal and the potential of machine learning models in optimizing the process. The results demonstrate the importance of pH, time, and catalyst amount in the removal process. The study contributes to advancements in intelligent decision-making and optimization of pollutant removal processes. The method is scalable for industrial applications and provides practical solutions for environmentally friendly pollutant removal. The integration of machine learning models with RSM offers a comprehensive approach to optimizing the photocatalytic process for cefixime removal. The study also employs optimization models like GA and Solver to find the best combination of parameters for achieving the highest removal efficiency. The results indicate that the SVR model outperforms the ANN model in terms of accuracy and performance. The study provides insights into the factors influencing cefixime removal and the effectiveness of the WO₃/Co-ZIF nanocomposite in the process. The findings have implications for environmental remediation and water pollution control. The research demonstrates the potential of combining nanotechnology with machine learning for efficient and sustainable pollutant removal.This study investigates the removal of cefixime from aqueous solutions using a WO₃/Co-ZIF nanocomposite and employs machine learning methods for modeling and optimization. The research aims to enhance the efficiency of cefixime degradation through intelligent decision-making and process optimization. Various models, including Support Vector Regression (SVR), Artificial Neural Network (ANN), Response Surface Methodology (RSM), Genetic Algorithm (GA), and Solver, were used to predict and optimize the removal process. The quadratic factorial model in RSM was selected as the best model for predicting results, with regression coefficients used to evaluate artificial intelligence models. The SVR model was found to be the best for prediction, with a high R² score (0.98) and low MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter. GA and Solver models were used to determine optimal conditions for cefixime removal, yielding optimal values of 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. The study highlights the effectiveness of the WO₃/Co-ZIF nanocomposite in cefixime removal and the potential of machine learning models in optimizing the process. The results demonstrate the importance of pH, time, and catalyst amount in the removal process. The study contributes to advancements in intelligent decision-making and optimization of pollutant removal processes. The method is scalable for industrial applications and provides practical solutions for environmentally friendly pollutant removal. The integration of machine learning models with RSM offers a comprehensive approach to optimizing the photocatalytic process for cefixime removal. The study also employs optimization models like GA and Solver to find the best combination of parameters for achieving the highest removal efficiency. The results indicate that the SVR model outperforms the ANN model in terms of accuracy and performance. The study provides insights into the factors influencing cefixime removal and the effectiveness of the WO₃/Co-ZIF nanocomposite in the process. The findings have implications for environmental remediation and water pollution control. The research demonstrates the potential of combining nanotechnology with machine learning for efficient and sustainable pollutant removal.