This　paper　proposes　a　new　technique　to　develop　an　accurate　multiphysics　parametric　model　for　microwave　components　to　speed　up　the　multiphysics　modeling　process.　In　the　proposed　technique,　the　artificial　neural　networks　(ANNs)　and　pole/residue　based　transfer　function　are　incorporated　to　represent　the　highly　non-linear　relationships　between　electromagnetic　centric　(EM-centric)　multiphysics　behaviors　and　multiphysics　geometrical/non-geometrical　design　parameters.　Vector　fitting　technique　is　utilized　to　obtain　the　poles/residues　of　the　transfer　function　for　each　multiphysics　sample.　Since　the　relationship　between　multiphysics　design　parameters　and　the　pole/residues　of　the　transfer　function　is　non-linear　and　unknown,　two　mapping　functions　are　proposed　to　establish　the　mathematical　links　between　the　multiphysics　design　parameters　and　poles/residues.　Parallel　multiphysics　data　generation　is　proposed　to　generate　the　training　and　testing　data　for　establishing　the　proposed　multiphysics　parametric　model.　A　two　stage　training　algorithm　is　proposed　to　guide　the　multiphysics　training　process.　Once　an　accurate　overall　model　is　developed,　it　can　be　used　to　provide　accurate　and　fast　prediction　of　the　multiphysics　behavior　of　microwave　components　with　geometrical　and　non-geometrical　parameters　as　variables,　and　further　can　be　used　in　the　high　level　design.　Compared　with　the　existing　multiphysics　modeling　methods,　the　proposed　technique　can　achieve　better　model　accuracy　with　high　efficiency.　The　proposed　technique　provides　an　accurate　and　efficient　methodology　even　when　the　coarse　model　or　empirical　model　is　unavailable.　Two　microwave　examples　are　used　to　illustrate　the　validity　of　the　proposed　multiphysics　parametric　modeling　technique.