Water-annulus　technology　is　regarded　as　a　promising　and　efficient　method　for　reducing　drag　and　saving　power　in　the　transportation　of　highly　viscous　oil.　Most　existing　theoretical　prediction　models　for　high-viscosity　oil-water　core-annular　flow　in　horizontal　pipes　are　based　on　a　concentric　core-annular　flow　configuration　with　a　circular　oil　core　and　a　smooth　oil-water　interface.　In　reality,　however,　this　ideal　configuration　may　not　be　achieved　due　to　the　buoyant　force　resulting　from　the　density　difference　between　the　oil　and　water,　and　the　instability　of　the　interface.　This　discrepancy　leads　to　a　significant　deviation　between　predicted　results　and　experimental　values　for　flow　characteristics,　particularly　the　pressure　gradient　and　water　holdup.　Therefore,　this　study　proposes　a　back　propagation　neural　network　model　enhanced　by　particle　swarm　optimization　to　predict　these　flow　characteristics　for　highly　viscous　oil-water　core-annular　flow　in　horizontal　pipes.　The　model　is　trained　and　tested　using　experimental　data　obtained　in　experimental　studies　reported　in　the　literature.　The　results　indicate　that　the　new　model　achieves　high　prediction　accuracy　for　both　pressure　gradients　and　water　holdups,　significantly　surpassing　the　accuracy　of　existing　phenomenological　models　for　core-annular　flow.　This　proposed　modeling　approach　has　the　potential　to　be　a　powerful　tool　for　design　and　flow　optimization　in　the　petroleum　industry.