The　flexible　bimodal　e-skin　exhibits　significant　promise　for　integration　into　the　next　iteration　of　human-computer　interactions,　owing　to　the　integration　of　tactile　and　proximity　perception.　However,　those　challenges,　such　as　low　tactile　sensitivity,　complex　fabrication　processes,　and　incompatibility　with　bimodal　interactions,　have　restricted　the　widespread　adoption　of　bimodal　e-skin.　Herein,　a　bimodal　capacitive　e-skin　capable　of　simultaneous　tactile　and　proximity　sensing　has　been　developed.　The　entire　process　eliminates　intricate　fabrication　techniques,　employing　DLP-3D　printing　for　the　electrode　layers　and　sacrificial　templating　for　the　dielectric　layers,　conferring　high　tactile　sensitivity　(1.672　kPa(-1))　and　rapid　response　capability　(similar　to　30　ms)　to　the　bimodal　e-skin.　Moreover,　exploiting　the　＂fringing　electric　field＂　effect　inherent　in　parallel-plate　capacitors　has　facilitated　touchless　sensing,　thereby　enabling　static　distance　recognition　and　dynamic　gesture　recognition　of　varying　materials.　Interestingly,　an　e-skin　sensing　array　was　created　to　identify　the　positions　and　pressure　levels　of　various　objects　of　different　masses.　Furthermore,　with　the　aid　of　machine　learning　techniques,　an　artificial　neural　network　has　been　established　to　possess　intelligent　object　recognition　capabilities,　facilitating　the　identification,　classification,　and　training　of　various　object　configurations.　The　advantages　of　the　bimodal　e-skin　render　it　highly　promising　for　extensive　applications　in　the　field　of　next-generation　human-machine　interaction.