Coastal　wetlands　are　critical　for　carbon　sequestration　and　shoreline　protection,　yet　their　rapid　transformation　demands　accurate　land-use　change　modeling　for　sustainable　management.　Traditional　Cellular　Automata　(CA)　models　often　neglect　landscape　structure,　limiting　their　ability　to　replicate　real-world　spatial　dynamics.　To　address　this　gap,　we　propose　a　landscape-enhanced　CA　(LE-CA)　model　that　integrates　landscape　metrics　into　the　simulation　framework.　The　LE-CA　model　couples　an　artificial　neural　network　(ANN)　to　assess　land-use　suitability,　a　Markov　module　to　estimate　transition　probabilities,　and　a　genetic　algorithm　(GA)　that　incorporates　landscape　indices　into　the　optimization　process.　The　framework　was　applied　to　the　Luoyangjiang　River　wetland　in　southeastern　China,　using　land-use　data　and　driving　factors　from　2018　to　2022.　Model　calibration　was　conducted　for　2018–2020　and　validation　for　2020–2022.　Comparative　analysis　with　conventional　models　(Markov-CA　and　ANN-Markov-CA)　revealed　that　the　LE-CA　model　achieved　superior　performance　in　both　overall　accuracy　(OA　=　0.8014)　and　figure　of　merit　(FoM　=　0.3548).　It　also　demonstrated　better　landscape　structural　similarity,　with　a　lower　RMSE　(0.4899)　and　reduced　combined　landscape　error　(1.2305)　during　validation.　These　results　highlight　the　LE-CA　model＇s　enhanced　ability　to　capture　complex　spatial　patterns　and　dynamic　land-use　processes.　By　embedding　landscape　structure　into　the　modeling　process,　the　LE-CA　framework　offers　a　more　realistic　and　reliable　approach　for　simulating　land-use　change　in　sensitive　coastal　wetland　ecosystems.　©　2025　Elsevier　B.V.