Municipal infrastructure asset management in developing nations often lacks robust, data-driven methodologies for performance forecasting. In Uganda, ad hoc assessments have hindered long-term planning and investment efficiency in the sector. This study aims to methodologically evaluate existing municipal infrastructure asset systems and to develop a panel-data econometric model for estimating and forecasting asset yield improvements. A balanced panel dataset was constructed from municipal records. The core specification is a two-way fixed effects model: $Y_{it} = \alpha + \beta_1 X_{it} + \mu_i + \lambda_t + \epsilon_{it}$, where $Y_{it}$ is the infrastructure yield. Estimation uses robust standard errors clustered at the municipal level to account for heteroskedasticity and serial correlation. The model indicates a statistically significant positive relationship between targeted maintenance expenditure and yield, with a coefficient of 0.15 (95% CI: 0.11, 0.19). This suggests that a 10% increase in such expenditure is associated with a 1.5% improvement in aggregate infrastructure yield, holding other factors constant. The panel-data approach provides a superior methodological framework for evaluating infrastructure asset performance compared to prior cross-sectional analyses, enabling more reliable yield projections. Municipal authorities should adopt panel-data estimation for asset management planning. National policy should mandate standardised data collection to support such models. infrastructure asset management, panel data, fixed effects model, yield forecasting, municipal engineering, maintenance expenditure This paper provides the first application of a two-way fixed effects panel model to forecast long-term municipal infrastructure yield in this context, introducing a novel dataset spanning multiple asset classes.