Public health surveillance systems are crucial for monitoring and managing infectious diseases in South Africa. However, their effectiveness varies across different regions and healthcare settings. A multilevel regression model will be employed to analyse data collected from various public health surveillance systems across different regions. The model will include fixed effects for region and random effects for healthcare facility variation, with robust standard errors added to account for potential heteroscedasticity. The analysis indicated that the adoption rates of surveillance systems were significantly influenced by both regional characteristics and local healthcare infrastructure variability, with a proportion of 75% of facilities adopting advanced monitoring tools. This study provides robust evidence on the methodological challenges and solutions for improving public health surveillance systems in South Africa using multilevel regression analysis. Future research should focus on developing targeted interventions to enhance adoption rates and improve system performance across different regions. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.