Planning irrigation development in northern Australia requires decision frameworks capable of evaluating trade-offs among ecosystem services, such as agricultural production, freshwater availability, flood regulation, water quality, habitat provision, and cultural values. This study presents a methodological framework that uses a Bayesian Network (BN) to analyse ecosystem-service trade-offs associated with staged irrigated agricultural development in the Gilbert River catchment. Rather than predicting outcomes, the approach functions as a structured decision-support tool that integrates diverse evidence sources within a transparent probabilistic framework. The BN architecture was developed through systematic identification of key system components spanning hydrological processes, land condition, vegetation dynamics, aquatic connectivity, soil function, and ecosystem-service endpoints. Causal pathways were defined using literature synthesis, regional datasets, policy documents, and expert knowledge. Variables were organised hierarchically from development drivers to pressures, ecological condition, and service outcomes, enabling explicit representation of indirect effects, cumulative pressures, and cross-system linkages. Conditional probability tables were parameterised using a combination of empirical evidence, modelling outputs, and structured expert elicitation so that uncertainty and data limitations are formally represented. The framework explicitly incorporates policy and land-management levers, such as environmental flow provisions, irrigation efficiency, nutrient and pesticide management, grazing pressure control, and riparian protection. These nodes enable testing of how alternative regulatory or management settings influence ecosystem-service trajectories under different development scenarios. Scenario nodes represent varying development intensities and intervention strengths, allowing comparison of plausible futures and identification of sensitive system components. Designed to be modular and updateable, the framework can incorporate new monitoring data over time to refine parameterisation and improve confidence. This study provides a transferable methodological template for analysing ecosystem-service trade-offs and supporting evidence-based water-resource planning in complex, data-limited socio-ecological systems.