Currently,　extensive　pipeline　networks　are　developed　in　response　to　the　demands　of　the　oil　and　gas　industry.　The　accurate　estimation　of　the　hydraulic　condition　of　pipeline　networks　holds　significant　importance　in　the　fields　of　pipeline　design　and　safety　management.　Nevertheless,　predicting　the　pressure　of　oil　and　gas　pipeline　networks　with　different　equipment　and　structures　remains　challenging.　To　meet　this　challenge,　a　novel　pressure　prediction　model　for　the　oil　and　gas　pipeline　networks　based　on　the　equipment　embedding　neural　network　(EENN)　is　proposed　in　this　study.　The　proposed　model　embeds　different　equipment　models　into　the　neural　network　model.　The　neural　network　in　this　model　is　used　to　focus　on　learning　the　connection　characteristics　of　the　pipeline　network　to　achieve　higher　prediction　accuracy.　The　present　study　first　explores　different　embedding　combinations　of　the　EENN　model　to　estimate　the　pressure　in　an　oil　pipeline　network　system　that　involves　a　non-isothermal　batch　transportation　process.　Then,　the　trained　model　is　applied　to　predict　the　pressure　in　a　gas　pipeline　network.　The　optimal　EENN　exhibits　an　average　prediction　error　of　18.5%　for　oil　pipelines　and　0.36%　for　gas　pipelines,　which　is　lower　than　20.8%　and　3.57%　under　the　neural　network.　The　findings　of　this　study　demonstrate　the　efficacy　of　the　proposed　EENN　in　accurately　forecasting　pressures　in　diverse　oil　and　gas　pipeline　networks　by　reducing　the　complexity　of　the　learning　process.