Hydrodynamic　models　are　commonly　used　to　understand　flood　risk　and　inform　flood　management　decisions.　However,　their　high　computational　cost　can　impose　practical　limits　on　real-time　flood　forecasting　and　uncertainty　analysis　which　require　fast　modelling　response　or　many　model　runs.　Emulation　models　have　the　potential　to　reduce　simulation　times　while　still　maintaining　acceptable　accuracy　of　the　estimates.　In　this　study,　we　propose　an　artificial　neural　networks　(ANNs)　based　emulation　modelling　framework　for　flood　inundation　modelling.　We　investigate　the　suitability　of　ANNs　as　flood　inundation　models　using　a　river　segment　in　Queensland,　Australia.　Our　results　show　that　ANNs　can　model　the　time　series　behaviour　of　flood　inundation　and　significantly　reduce　the　simulation　times　required,　which　facilitates　their　use　in　applications　requiring　fast　model　response　or　a　large　number　of　model　runs.　Based　the　model　development　process　and　results,　the　major　challenges　and　future　research　directions　are　discussed.