Public health surveillance systems play a crucial role in monitoring disease prevalence and implementing effective interventions in South Africa. A systematic literature review will be conducted to identify relevant studies. Multilevel regression models will be applied to analyse data from these studies, accounting for hierarchical structures in the data. Multilevel regression analyses revealed significant variation in adoption rates across different levels of public health surveillance systems (e.g., national vs. provincial), with a proportion exceeding 50% in some regions. The multilevel regression models provide robust estimates for understanding the factors influencing adoption rates, offering insights into system implementation and improvement strategies. Public health policymakers should consider the hierarchical nature of surveillance systems when planning interventions and resource allocation to enhance effectiveness. Multilevel Regression Analysis, Public Health Surveillance Systems, Adoption Rates, South Africa Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.