AI‐Driven Modeling and Statistical Assessment of AgO/ZnO/g‐C₃N₄ Photocatalysts on Wastewater Treatment: Impact of UV–Visible Light

Minimizing the environmental impact of dye‐laden wastewater, remains a critical challenge with high economic implications. This study focuses on the development and optimization of g‐C3N4‐based photocatalysts doped with AgO and ZnO at varying Zn (0%–100%) and Ag (0%–2.5%) loadings. Catalysts were applied at four dosages (0.03–0.12 g/100 mL), and their methylene blue degradation efficiencies were evaluated over time intervals up to 3 h. Photocatalysts were synthesized using both conventional and ultrasound‐assisted (US‐assisted) co‐precipitation methods. The US‐assisted synthesis yielded improved morphology and dispersion, as evidenced by SEM‐EDS and XRD analyses, and enhanced photocatalytic performance. Experimental data were used to train three AI models; artificial neural network (ANN), support vector regression (SVR), and Random forest (RF). SVR exhibited the highest predictive accuracy (R 2 = 0.9854, RMSE = 0.0401), while ANN and RF also showed strong performance (R 2 ≈ 0.980). Model robustness was validated through residual analysis and statistical tests. To assess the influence of input variables, one‐way and multi‐factor Type II ANOVA were conducted. Zn and Ag content, catalyst dosage, and reaction time were all statistically significant ( p < 0.05), with US treatment and Zn loading having the most dominant effects. Ag's contribution was also significant but more composition‐dependent.