Accurate forecasting of clinical outcomes is critical for resource planning and quality improvement in emergency care, yet robust methodological frameworks for low-resource settings are lacking. This study aimed to develop and methodologically evaluate a bespoke time-series forecasting model for key clinical outcomes within the Ghanaian emergency care context. We utilised longitudinal, de-identified patient data from multiple urban emergency units. The core forecasting model was a seasonal autoregressive integrated moving average (SARIMA) formulation: $\phi(B)\Phi(B^s)\nabla^d\nabla^D_s Y_t = \theta(B)\Theta(B^s)\epsilon_t$, where $Y_t$ represents the clinical outcome series. Model performance was evaluated against historical data using mean absolute scaled error (MASE) and 95% prediction intervals. The SARIMA(1,1,1)(0,1,1)7 model for patient mortality forecasts demonstrated superior performance (MASE = 0.76), with prediction intervals reliably capturing observed volatility. A key finding was a consistent 14-day cyclical pattern in critical admissions, strongly associated with forecast accuracy. The proposed time-series model provides a statistically sound and operationally feasible tool for forecasting emergency care outcomes in this setting, addressing a significant methodological gap. Implementation of this forecasting approach in hospital management systems is recommended to guide staffing and supply chain logistics. Further research should integrate exogenous variables like disease surveillance data. forecasting, clinical outcomes, emergency care, time-series analysis, health systems, Ghana This paper presents a novel, context-adapted forecasting methodology validated for emergency care systems in a low-resource setting, offering a new tool for predictive health service analytics.