Thermal　barrier　coatings　(TBCs)　play　a　crucial　role　in　safeguarding　aero-engine　blades　from　high-temperature　environments　and　enhancing　their　performance　and　durability.　Accurate　evaluation　of　TBCs＇　porosity　is　of　paramount　importance　for　aerospace　material　research.　However,　existing　evaluation　methods　often　involve　destructive　testing　or　lack　precision.　In　this　study,　we　proposed　a　novel　nondestructive　evaluation　method　for　TBCs＇　porosity,　utilizing　terahertz　time-domain　spectroscopy　(THz-TDS)　and　a　machine　learning　approach.　The　primary　objective　was　to　achieve　reliable　and　precise　porosity　evaluation　without　causing　damage　to　the　coatings.　Multiple　feature　parameters　were　extracted　from　THz-TDS　data　to　characterize　porosity　variations.　Additionally,　correlation　analysis　and　p-value　testing　were　employed　to　assess　the　significance　and　correlations　among　the　feature　parameters.　Subsequently,　the　dung-beetle-optimizer-algorithm-optimized　random　forest　(DBO-RF)　regression　model　was　applied　to　accurately　predict　the　porosity.　Model　performance　was　evaluated　using　K-fold　cross-validation.　Experimental　results　demonstrated　the　effectiveness　of　our　proposed　method,　with　the　DBO-RF　model　achieving　high　precision　and　robustness　in　porosity　prediction.　The　model　evaluation　revealed　a　root-mean-square　error　of　1.802,　mean　absolute　error　of　1.549,　mean　absolute　percentage　error　of　8.362,　and　average　regression　coefficient　of　0.912.　This　study　introduces　a　novel　technique　that　presents　a　dependable　nondestructive　testing　solution　for　the　evaluation　and　prediction　of　TBCs＇　porosity,　effectively　monitoring　the　service　life　of　TBCs　and　determining　their　effectiveness.　With　its　practical　applicability　in　the　aerospace　industry,　this　method　plays　a　vital　role　in　the　assessment　and　analysis　of　TBCs＇　performance,　driving　progress　in　aerospace　material　research.