The　distinctive　characteristics　of　electrically　conductive　fabrics,　including　their　flexibility,　breathability,　and　comfort,　have　led　to　their　recognition　as　a　viable　substitute　for　silicon　wafers　in　wearable　electronics.　However,　the　difficulty　of　constructing　sensors　with　three-dimensional　(3D)　structure　on　woven　fabrics　significantly　limits　their　sensitivity　and　sensing　range.　Layer-by-layer　3D　printing　of　entire　smart　textile　sensing　components　has　enabled　the　development　of　high-performance　sensors　with　enhanced　sensitivity　and　sensing　range.　This　research　endeavors　to　produce　a　smart　glove　with　superior　performance　by　incorporating　strain　and　pressure　sensors　by　3D　printing　a　composite　conductive　ink,　consisting　of　multi-walled　carbon　nanotubes　(MWCNTs),　graphene　nanosheets　(GNSs),　fumed　silica　(FSiO2)　and　Ecoflex,　and　encapsulated　ink　directly　onto　a　commercially　available　fabric　glove.　The　3D　structure　of　the　sensing　layer　and　the　sensing　material　were　intentionally　designed　to　achieve　desired　performance.　The　smart　glove　demonstrates　a　high　gauge　factor　(GF　∼　35)　and　a　strain　range　of　0–50%　for　strain　detection.　Additionally,　it　exhibits　a　high　sensitivity　of　∼0.07　kPa−1　and　a　sensing　range　of　1000　kPa　for　pressure　examination,　which　facilitates　precise　detection　of　finger　bending　angles　and　fingertip　contact　pressures.　The　smart　glove　also　shows　excellent　linearity,　repeatable　resistance　response,　favorable　cycling　characteristics　in　both　strain　and　pressure　detecting,　and　were　unaffected　by　temperature　and　humidity.　The　combination　of　the　smart　glove　with　a　Long　Short-Term　Memory　(LSTM)　deep　learning　model　achieves　a　high　accuracy　(100%)　for　dynamic　gesture　recognition　and　manipulator　control,　demonstrating　their　potential　for　smart　wearable　electronics　and　human-computer　interaction.　©　2023