Public health surveillance systems are critical for disease control and health policy, yet methodological frameworks for evaluating their adoption and forecasting future uptake are underdeveloped, particularly in resource-limited settings. This study aimed to methodologically evaluate the adoption of public health surveillance systems and to develop a robust time-series forecasting model for predicting future adoption rates. We conducted a longitudinal analysis of national surveillance system deployment data. The core forecasting model was an autoregressive integrated moving average (ARIMA) model, specified as $\Delta^d y_t = c + \sum_{i=1}^{p}\phi_i \Delta^d y_{t-i} + \sum_{j=1}^{q}\theta_j \epsilon_{t-j} + \epsilon_t$, where parameters were estimated using maximum likelihood. Model diagnostics included checks for residual autocorrelation and heteroskedasticity. The model forecast indicates a significant deceleration in the annual adoption rate, projecting a decline to approximately 3.2% per annum by the end of the forecast period (95% CI: 2.1% to 4.3%). This slowdown was robust to alternative model specifications and the inclusion of structural break tests. The methodological evaluation reveals that the adoption trajectory of surveillance systems is entering a phase of markedly reduced growth, which may jeopardise public health preparedness if unaddressed. Policy must shift from initial deployment to addressing systemic barriers sustaining adoption, including targeted training, interoperable data standards, and dedicated operational funding. surveillance systems, adoption forecasting, time-series analysis, health informatics, implementation science This paper provides a novel methodological framework integrating system evaluation with statistical forecasting, generating the first long-term adoption projections for public health surveillance infrastructure in this context.