Thermal　barrier　coatings　(TBCs)　are　usually　used　in　high　temperature　and　harsh　environment,　resulting　in　thinning　or　even　spalling　off.　Hence,　it　is　vital　to　detect　the　thickness　of　the　TBCs.　In　this　study,　a　hybrid　machine　learning　model　combined　with　terahertz　time-domain　spectroscopy　technology　was　designed　to　predict　the　thickness　of　TBCs.　The　terahertz　signals　were　obtained　from　the　samples　prepared　in　laboratory　and　actual　turbine　blade.　The　principal　component　analysis　(PCA)　method　was　used　to　decrease　the　data　dimensions.　Finally,　an　extreme　learning　machine　(ELM)　was　proposed　to　establish　the　thickness　of　TBCs　prediction　model.　Genetic　algorithm　(GA)　was　selected　to　optimize　the　model　to　make　it　more　accurate.　The　results　showed　that　the　root　correlation　coefficient　(R-2)　exceeded　0.97　and　the　errors　(root　mean　square　error　and　mean　absolute　percentage　error)　were　less　than　2.57.　This　study　proposes　that　terahertz　time-domain　technology　combined　with　PCA-GA-ELM　model　is　accurate　and　feasible　for　evaluating　the　thickness　of　the　TBCs.