Abstract
The growing occurrence of antibiotic residues in South African water systems poses serious environmental and
public health risks, owing mostly to pharmaceutical discharge, agricultural runoff, and poor waste management.
Conventional water treatment procedures frequently fail to properly remove these micropollutants, needing new
predictive and analytical approaches. This review critically investigates the implementation of Artificial Neural
Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models to forecast and optimize antibiotic
removal from South African water bodies. To the best of our knowledge, little or no research compares the
models’ respective performances in the context of the urban water cycle in South Africa. Therefore, this review
elaborates on some of the pharmaceuticals (such as diclofenac sodium and tetracycline) that have been studied,
as well as the challenges associated with their removal. It also emphasizes studies on modeling and predicting
pharmaceutical removal from wastewater using ANN and ANFIS models. Additionally, this review considered the
comparisons between ANN and ANFIS models in predicting the removal of emerging contaminants, as well as the
challenges and limitations associated with these modeling techniques. The studies established that AI models
achieved higher R² and lower error metrics compared to classical statistical or isotherm models.