Amman, Jordan, faces escalating challenges in municipal solid waste management due to rapid urbanisation, population growth, and limited recycling participation. This study develops a novel decision-support framework that combines a System Dynamics (SD) model with fuzzy Analytic Hierarchy Process (AHP) and Artificial Neural Network (ANN) validation. The model explicitly integrates population trends, social awareness, recycling initiatives, and informal waste picker contributions into municipal waste forecasting. Social awareness was quantified using a fuzzy AHP survey of households, and its influence on source sorting behaviours was embedded as a dynamic feedback variable. Model validation demonstrates that the SD model achieved 94.3% prediction accuracy, outperforming the ANN’s 91.5%. Scenario testing showed that increasing household participation in source sorting by 20% annually reduced the growth rate of waste generation from 2.56% to 1.38%, diverting more than 60,000 tonnes from landfill by 2030. These findings underscore the critical role of behavioural interventions, beyond technical solutions, in reducing landfill dependency. By embedding social and behavioural variables within a predictive modelling framework, this study contributes a replicable tool for policymakers in Amman and other rapidly urbanising cities to advance circular economy strategies and long-term landfill planning. © The Author(s) 2025.

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