<p>This study reports the statistical optimization and catalytic evaluation of copper nanoparticles integrated with graphene oxide for hydrogen peroxide–assisted degradation of phenol red. The current research innovates through a novel integration of statistical synthesis optimization with catalytic performance evaluation, enabling a direct correlation between synthesis parameters and catalytic efficiency. In addition, graphene oxide is demonstrated to play a dual role as both a stabilizing support and an electron-transfer mediator, significantly enhancing nanoparticle dispersion and catalytic activity. Copper nanoparticle synthesis was optimized using response surface methodology based on a central composite design, establishing optimal conditions at a pectin concentration of 0.6 g/L, an ascorbic acid-to-copper precursor molar ratio of 2.3, and a precursor concentration of 24.1 mM. To synthesize the composite, optimized nanoparticles were loaded onto graphene oxide at a concentration of 2.0 mg/100 mL. The resulting composite exhibited an average particle size of 65.1 nm and a remarkable specific surface area of 47.3 m²/g compared to just 4.4 m²/g for the bare particles. Catalytic degradation experiments, further optimized statistically, identified optimal operational parameters at a catalyst dosage of 43.5 ppm and a hydrogen peroxide concentration of 1.25%. Using the identified optimal conditions, the model predicted a phenol red conversion of 98%, which closely matched the actual experimental value of 95% within 90 min. A coefficient of determination of 0.974 confirmed the goodness of fit and high reliability of the optimization model. Radical scavenging analysis confirmed a heterogeneous Fenton-like oxidation pathway involving hydroxyl, superoxide, and hydroperoxide radicals. The experimental evidence demonstrates that coupling statistical synthesis design with composite engineering significantly enhances catalytic efficiency for the oxidative removal of persistent phenolic contaminants from wastewater.</p>