-　Virtual　Reality　(VR)　technology　has　shown　promise　in　simulating　music　scenes　for　emotion　regulation.　The　challenge　is　to　enhance　the　realism　and　interactivity　of　VR-based　music　experiences　to　create　a　more　immersive　and　emotionally　impactful　environment　for　users.　This　manuscript　proposes　an　augmented　physics　-informed　neural　network　and　seahorse　optimization　algorithm　for　music　scene　simulation　and　emotion　regulation　in　virtual　reality　technology　(APINN-SHO-MSS-ERVRT).　Initially,　the　data　is　collected　via　head　-mounted　display　(HMD)　in　real　time　basis.　Afterward,　the　data　is　fed　to　data　-adaptive　Gaussian　average　filtering　(DAGAF)　based　pre-processing　process.　In　the　pre-processing　segment,　it　improves　virtual　presence.　The　preprocessing　output　is　given　to　augmented　physics　-informed　neural　network　(APINN)and　seahorse　optimization　algorithm　(SHO)　for　effectively　classifying　the　facial　expression　for　fear　and　null.　Therefore,　SHO　is　proposed　to　enhance　weight　parameter　of　augmented　physics　-informed　neural　network　classifier,　which　precisely　classifies　the　facial　emotion　classifier.　The　proposed　method　is　implemented　in　MATLAB　and　evaluated　their　performance　with　existing　methods.　The　performance　metrics,　like　accuracy,　precision,　sensitivity,　specificity,　computational　time　and　ROC　is　analysed　to　the　performance　of　the　proposed　method.　The　proposed　APINN-SHO-MSS-ERVRT　methods　of　accuracy　are　provide　90%　higher　accuracy　for　fear　and　97%　higher　accuracy　for　null　emotion　is　analysed　when　compared　with　existing　methods　GERR-NN-AMSVR,　ER-VR-EEG　and　MTS-VRS-CDI.　Proposed　APINN-SHO-MSS-ERVRT　method　attains　0.87%,　0.88%　and　0.89%higher　ROC　analysed　to　the　existing　methods　respectively.