The　simulation　of　vocal　performance　has　emerged　as　an　important　means　of　training　singers　in　a　more　engaging　manner　than　standard　methods　of　vocal　coaching.　The　purpose　of　this　research　is　to　present　a　new　vocal　performance　simulation　and　training　system　developed　using　neural　networks　(NN)　in　a　virtual　reality　(VR)　environment　and　to　improve　the　training　of　vocal　performance　using　advanced　NN　models　and　VR.　For　this　purpose,　the　researcher＇s　audio　is　primarily　to　analyze,　collect　and　process　samples　recording　performance　as　well　as　specific　performers＇　training　audio-video　material.　Preprocessing　techniques　such　as　noise　reduction,　and　normalization,　and　applied　to　prepare　the　data.　Key　features　like　pitch,　tone,　and　breath　control　were　extracted　using　the　Mel-frequency　cepstral　coefficients　(MFCC)　algorithm,　enabling　effective　feature　representation.　We　propose　the　Refined　Fruit　Fly　Optimized　Intelligent　Long-Short　Term　Memory　(RFF-ILSTM)　model,　a　recurrent　neural　network　(RNN)-based　approach　optimized　for　handling　sequential　vocal　data　with　high　precision.　The　model　incorporates　the　RFF　optimization　technique　in　order　to　enhance　the　tuning　of　the　LSTM　architecture　thereby　increasing　its　speed　of　convergence　and　training.　The　information　collected　was　already　processed　by　the　proposed　framework　in　an　effort　to　enhance　vocal　performance　simulation.　VR　integration　enhances　the　enjoyment　and　effectiveness　of　the　interface　as　it　allows　singers　to　receive　immediate　feedback　as　if　they　were　performing　in　front　of　an　audience.　Users　engage　in　performing　activities　based　on　3D　images,　thereby　allowing　the　practice　of　different　types　of　vocations　without　difficulty.　Simulation　results　showed　dramatic　improvements　in　terms　of　ability　to　control　vocals　and　tones,　as　well　as　consistency　in　performance.　The　system　successfully　integrates　VR　and　NN　enhancement　for　an　improved,　interactive　system　of　training　for　vocal　performances.　　©　2025　World　Scientific　Publishing　Company.