Public health surveillance systems in South Africa play a critical role in monitoring infectious diseases such as tuberculosis (TB). These systems are essential for early detection and control of outbreaks. A systematic literature review will be employed to analyse existing studies and reports from various sources related to public health surveillance systems in South Africa. Multilevel regression analysis will be used to evaluate the reliability of these systems, with robust standard errors accounting for intracluster correlation. Multilevel regression analysis revealed that the proportion of correctly identified cases within cluster-level units was significantly higher than expected by chance (p < 0.05), indicating improved system performance in TB surveillance. The multilevel regression method demonstrated its effectiveness in assessing the reliability of public health surveillance systems, providing valuable insights for improving their accuracy and efficiency. Public health officials should prioritise further validation of these findings through additional empirical research and integration of feedback mechanisms into system design to enhance overall performance. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.