Public health surveillance systems are crucial for monitoring diseases and managing outbreaks in Tanzania. However, their adoption rates vary significantly across different regions. A quasi-experimental approach was employed, including surveys and interviews with healthcare professionals across various regions. Data were analysed using logistic regression to predict adoption rates based on demographic and infrastructure variables. Adoption of surveillance systems varied considerably by region; urban areas showed higher adoption rates (75%) compared to rural areas (40%). Infrastructure quality was the strongest predictor of system adoption, with a coefficient of $β = 1.2$ in logistic regression analysis. The quasi-experimental design successfully identified regions with high and low adoption rates, informing targeted interventions for improving surveillance systems across Tanzania. Policy recommendations include investing in infrastructure improvements to enhance system adoption and prioritising implementation strategies that address regional disparities.