Titanium dioxide (TiO₂) based photocatalysts have been widely used as a photocatalyst for the degradation of various persistent organic compounds in water and air. The degradation mechanism involves the generation of highly reactive oxygen species, such as hydroxyl radicals, which react with organic compounds to break down their chemical bonds and ultimately mineralize them into harmless products. In the case of pharmaceutical and pesticide molecules, TiO₂ and modified TiO₂ photocatalysis effectively degrade a wide range of compounds, including antibiotics, pesticides, and herbicides. The main downside is the production of dangerous intermediate products, which are not frequently addressed in the literature that is currently available. The degradation rate of these compounds by TiO₂ photocatalysis depends on factors such as the chemical structure of the compounds, the concentration of the TiO₂ catalyst, the intensity, the light source, and the presence of other organic or inorganic species in the solution. The comprehension of the degradation mechanism is explored to gain insights into the intermediates. Additionally, the utilization of response surface methodology is addressed, offering a potential avenue for enhancing the scalability of the reactors. Overall, TiO₂ photocatalysis is a promising technology for the treatment of pharmaceutical and agrochemical wastewater, but further research is needed to optimize the process conditions and to understand the fate and toxicity of the degradation products.

Harnessing the synergistic potential of graphene oxide-doped titanium dioxide (GO-TiO₂), this study pioneers an advanced photocatalytic approach by incorporating graphene oxide-doped titanium dioxide (GO-TiO₂) as a catalyst to enhance the photocatalytic degradation of levofloxacin (LVX), with optimisation of parameters using response surface methodology (RSM) and artificial neural networks (ANNs). By adjusting key operational parameters such as catalyst dosage, LVX concentration, pH, and percentage dopant in TiO₂, the study aimed to maximise degradation efficiency. The RSM statistical model highlighted optimal conditions, i.e., neutral pH, 0.1 g/g dopant, 1.1 g/L catalyst, and 25 ppm LVX concentration, achieving a degradation efficiency close to 80% (R² = 0.88). An ANN model was also developed, offering a three-layer neural network that accurately predicts LVX degradation under varied conditions, with R² reaching 0.97. Current modelling techniques frequently fail to strike a balance between practical insights for optimising photocatalytic degradation and predictive accuracy. By combining the parametric insights of RSM with the nonlinear predictive power of ANN, this study closes that gap and develops a sustainable, data-driven framework for effectively breaking down pharmaceutical pollutants and developing environmentally friendly wastewater treatment methods.