Cancer　is　a　lethal　illness　that　requires　an　initial　stage　prognosis　to　enhance　patient　survival　rate.　Accurate　brain　tumor　and　their　sub-structure　segmentation　through　Magnetic　Resonance　Images　(MRIs)　is　a　tough　endeavor.　Owing　to　the　heterogeneous　tumor　areas,　automatically　segmenting　brain　tumors　has　proved　to　be　a　critical　task　even　for　neural　network-based　algorithms,　some　tumor　regions　remain　unidentified　due　to　their　small　size　and　the　variation　in　area　occupancy　among　tumor　sub-classes.　Current　progress　in　the　area　of　neural　networks　has　been　employed　to　enhance　the　segmentation　performance.　This　study　designed　an　intelligent　3D　U-Net　encoder-decoder-based　system　for　automatic　detection　and　brain　tumor　sub-structure　segmentation.　Our　proposed　3D　model　constitutes　neural　units　(the　basic　building　blocks)　followed　by　transition　layer　blocks　and　skip　connections.　BraTS　2018　and　private　local　datasets　are　used　to　evaluate　the　proposed　model　which　segments　the　Whole　Tumor　(WT),　Tumor　Core　(TC),　and　the　Enhancing　Tumor　(ET).　The　training　accuracy,　validation　accuracy,　dice　score,　sensitivities,　and　specificities　of　WT,　CT,　and　ET　zones　are　computed.　The　experimental　results　demonstrate　that　dice　scores　are　0.913,　0.874,　and　0.801　for　the　BraTS　2018　dataset.　The　developed　models　performance　was　further　evaluated　by　utilizing　the　dataset　from　a　local　hospital　containing　71　subjects.　The　dice　scores　of　0.891,　0.834,　and　0.776　are　achieved　by　the　proposed　model　on　the　private　dataset.　The　practicability　of　the　proposed　model　was　assessed　by　the　comparative　studies　of　our　model　with　existing　literature.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2024.