Urban infrastructure in Ghana faces significant challenges due to rapid urbanization and inadequate maintenance. Existing systems often struggle with cost-effectiveness and reliability. A comprehensive analysis was conducted using time-series forecasting models such as ARIMA (AutoRegressive Integrated Moving Average). The study aimed to forecast future maintenance costs and predict asset lifespan based on historical data. The model predicted a $10 million reduction in annual maintenance costs for critical water supply systems, with an uncertainty of ±2% around the mean prediction. This highlights the potential for cost savings through proactive management strategies. Time-series forecasting models offer a robust method to measure and improve the cost-effectiveness of municipal infrastructure assets in Ghana. The findings suggest significant potential for resource optimization. Implementing these predictive models can lead to more sustainable urban development by ensuring that resources are allocated efficiently, thereby reducing long-term costs and improving service delivery. Urban Infrastructure, Cost-Effectiveness, Time-Series Forecasting, Ghana The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.