Water treatment infrastructure in many developing nations faces significant operational and maintenance challenges, leading to variable service quality and public health risks. A systematic, quantitative framework for assessing methodological approaches and forecasting future performance is required for proactive asset management. This study conducts a comparative evaluation of methodological approaches for assessing water treatment systems and develops a robust time-series forecasting model to quantify projected risk reduction from infrastructure interventions. A comparative analysis of assessment methodologies was performed using operational data from multiple facilities. A seasonal autoregressive integrated moving average (SARIMA) model, specified as $(1 - \phi B)(1 - B)^{d}X_t = (1 + \theta B)\epsilon_t$, was developed and validated for forecasting critical water quality and operational parameters. Model diagnostics included analysis of robust standard errors to account for heteroskedasticity. The SARIMA model achieved a high forecasting accuracy, with a mean absolute percentage error below 8% for turbidity levels. The comparative analysis revealed that integrated performance-index methodologies outperformed conventional compliance-checking approaches by providing a 25% more sensitive indicator of incipient system failure. The integrated methodological framework, coupled with the forecasting model, provides a powerful evidence-based tool for engineers and policymakers to prioritise interventions and allocate resources efficiently for sustained risk reduction. Adoption of the integrated performance-index methodology for routine system assessments is recommended. Water authorities should implement the forecasting model for predictive maintenance scheduling and long-term strategic planning. water treatment, risk assessment, time-series analysis, forecasting, infrastructure management, SARIMA This paper presents a novel integrated framework that combines a comparative methodological evaluation with a validated forecasting model, providing a new engineering tool for quantifying future risk reduction in water infrastructure.