In　the　present　work,　artificial　neural　networks　have　been　developed　to　predict　the　relationship　and　influence　of　shear　rate/temperature　and　particle　loading　on　the　viscosity,　density,　thermal　conductivity　and　isobaric　specific　heat　capacity　of　Al2O3　nanoparticles　dispersed　in　a　binary　mixture　of　water　and　ionic　liquid　([C2mim][CH3SO3]/water).　The　properties　of　the　alumina　nanoparticles　enhanced　ionic　liquids　with　respect　to　the　base　fluids　have　been　modeled　using　feed-forward　back-propagation　(BP)　ANNs.　The　study　has　disclosed　that　the　developed　models　predict　the　thermophysical　properties　of　NEILs　with　reasonable　accuracy.　The　regression　coefficient　(R)　of　developed　models　is　noted　to　be　greater　than　0.99.　Moreover,　the　root　mean　square　errors　for　the　developed　models　were　found　to　be　in　the　range　of　0.0007–0.081,　revealing　an　excellent　compliance　between　the　experimental　and　calculated　thermophysical　properties　of　NEILs.　©　2021　Elsevier　B.V.