The　process　of　UUV　delivery　is　a　typical　nonlinear　transient　dynamic　phenomenon,　which　is　generally　described　by　the　internal　ballistic　model.　Evaluation　of　optimal　internal　ballistics　parameters　is　a　key　step　for　promoting　ballistic　weapon　performance　under　given　launch　constraints.　Hence,　accurate　and　efficient　optimization　techniques　are　required　in　ballistics　technology.　In　this　study,　an　artificial　neural　network　(ANN)　is　used　to　simplify　the　process　of　regression　analysis.　To　this　end,　an　internal　ballistics　model　is　built　in　this　study　as　a　black　box　for　a　classic　underwater　launching　system,　such　as　a　torpedo　launcher,　based　on　ANN　parameter　identification.　The　established　black　box　models　are　mainly　employed　to　calculate　the　velocity　of　a　ballistic　body　and　the　torque　of　a　launching　pump.　Typical　internal　ballistics　test　data　are　adopted　as　samples　for　training　the　ANN.　Comparative　results　demonstrate　that　the　developed　black　box　models　can　accurately　reflect　changes　in　internal　ballistics　parameters　according　to　rotational　speed　variations.　Therefore,　the　proposed　approach　can　be　fruitfully　applied　to　the　task　of　internal　ballistics　optimization.　The　optimization　of　internal　ballistics　precision　control,　optimal　control　of　the　launching　pump,　and　optimal　low-energy　launch　control　were,　respectively,　realized　in　conjunction　with　the　established　model　using　the　SHERPA　search　algorithm.　The　results　demonstrate　that　the　optimized　internal　ballistics　rotational　speed　curve　can　achieve　the　optimization　objectives　of　low-energy　launch　and　peak　power　while　meeting　the　requirements　of　optimization　constraints.