The　escalating　greenhouse　gas　(GHG)　emissions　from　maritime　trade　present　a　serious　environmental　and　biological　threat.　With　increasing　emission　reduction　initiatives,　such　as　the　European　Union＇s　incorporation　of　the　maritime　sector　into　the　emissions　trading　system,　both　challenges　and　opportunities　emerge　for　maritime　transport　and　associated　industries.　To　address　these　concerns,　this　study　presents　a　model　specifically　designed　for　estimating　and　projecting　the　spatiotemporal　GHG　emission　inventory　of　ships,　particularly　when　dealing　with　incomplete　automatic　identification　system　datasets.　In　the　computational　aspect　of　the　model,　various　data　processing　techniques　are　employed　to　rectify　inaccuracies　arising　from　incomplete　or　erroneous　AIS　data,　including　big　data　cleaning,　ship　trajectory　aggregation,　multi-source　spatiotemporal　data　fusion　and　missing　data　complementation.　Utilizing　a　bottom-up　ship　dynamic　approach,　the　model　generates　a　high-resolution　GHG　emission　inventory.　This　inventory　contains　key　attributes　such　as　the　types　of　ships　emitting　GHGs,　the　locations　of　these　emissions,　the　time　periods　during　which　emissions　occur,　and　emissions.　For　predictive　analytics,　the　model　utilizes　temporal　fusion　transformers　equipped　with　the　attention　mechanism　to　accurately　forecast　the　critical　emission　parameters,　including　emission　locations,　time　frames,　and　quantities.　Focusing　on　the　sea　area　around　Tianjin　port-a　region　characterized　by　high　shipping　activity-this　study　achieves　fine-grained　emission　source　tracking　via　detailed　emission　inventory　calculations.　Moreover,　the　prediction　model　achieves　a　promising　loss　function　of　approximately　0.15　under　the　optimal　parameter　configuration,　obtaining　a　better　result　than　recurrent　neural　network　(RNN)　and　long　short-term　memory　network　(LSTM)　in　the　comparative　experiments.　The　proposed　method　allows　for　a　comprehensive　understanding　of　emission　patterns　across　diverse　vessel　types　under　various　operational　conditions.　Coupled　with　the　prediction　results,　the　study　offers　valuable　theoretical　and　data-driven　support　for　formulating　emission　reduction　strategies　and　optimizing　resource　allocation,　thereby　contributing　to　sustainable　maritime　transformation.