Water treatment systems in many developing nations face chronic underperformance, leading to unreliable supply. Accurate forecasting of system yield is critical for infrastructure planning and operational management, yet robust, context-specific models are often lacking. This study aimed to develop and evaluate a novel time-series forecasting model to predict treated water yield, thereby providing a methodological framework for performance improvement in treatment facilities. A seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model was developed, formalised as $\phi(B)\Phi(B^s)\nabla^d\nabla_s^D y_t = \theta(B)\Theta(B^s)\epsilon_t + \beta X_t$, where $X_t$ represents rainfall and operational expenditure. The model was trained and validated using high-frequency operational data from multiple facilities. The model achieved a mean absolute percentage error of 8.7% on test data, with a 95% confidence interval for one-year-ahead forecasts indicating a potential yield improvement of 12–18% through optimised chemical dosing schedules aligned with predicted raw water quality. The proposed SARIMAX model provides a statistically robust and operationally actionable tool for forecasting treated water yield, demonstrating superior accuracy over conventional moving-average approaches in this context. Water utilities should integrate this forecasting methodology into their asset management systems to enable proactive maintenance and resource allocation. Further research should focus on real-time model integration using supervisory control and data acquisition (SCADA) systems. water treatment yield, time-series analysis, SARIMAX, forecasting, infrastructure performance, operational management This paper presents a novel application of a SARIMAX model incorporating local climatic and operational drivers to forecast water treatment yield, providing a new evidence-based tool for engineers managing similar systems in resource-constrained settings.