The　COVID-19　pandemic　highlighted　significant　challenges　in　maritime　industries,　such　as　port　congestion　and　supply　chain　disruptions,　necessitating　accurate　port　traffic　prediction.　Traditional　models　often　fail　to　capture　the　complex　and　dynamic　interactions　within　maritime　networks,　particularly　the　spatial　correlations　between　ports.　To　address　these　limitations,　this　study　proposes　a　spatio-temporal　graph　neural　network　(MSTGNN)　tailored　for　maritime　traffic　prediction.　MSTGNN　integrates　temporal　and　spatial　features　using　a　sample　convolution　and　interaction　network　(SCINet)　and　a　graph　attention　network　(GAT),　enhanced　by　a　k-core　mechanism　for　prioritizing　key　ports.　Extensive　experiments　with　global　and　regional　datasets　demonstrate　MSTGNN＇s　superior　performance　in　accuracy,　stability,　and　adaptability　compared　to　existing　models.　The　model　excels　in　predicting　traffic　flows　across　different　ship　types,　making　it　highly　applicable　for　optimizing　port　operations,　route　planning,　and　mitigating　supply　chain　risks.　The　findings　imply　that　MSTGNN　can　significantly　enhance　decision-making　in　maritime　logistics　by　offering　more　precise　traffic　predictions,　thereby　supporting　more　efficient,　resilient,　and　sustainable　global　supply　chains.