In　cold　regions,　the　dynamic　compressive　strength　(DCS)　of　rock　damaged　by　freeze-thaw　weathering　significantly　influences　the　stability　of　rock　engineering.　Nevertheless,　testing　the　dynamic　strength　under　freeze-thaw　weathering　conditions　is　often　both　time-consuming　and　expensive.　Therefore,　this　study　considers　the　effect　of　characteristic　impedance　on　DCS　and　aims　to　quickly　determine　the　DCS　of　frozen-thawed　rocks　through　the　application　of　machine-learning　techniques.　Initially,　a　database　of　DCS　for　frozen-thawed　rocks,　comprising　216　rock　specimens,　was　compiled.　Three　external　load　parameters　(freeze-thaw　cycle　number,　confining　pressure,　and　impact　pressure)　and　two　rock　parameters　(characteristic　impedance　and　porosity)　were　selected　as　input　variables,　with　DCS　as　the　predicted　target.　This　research　optimized　the　kernel　scale,　penalty　factor,　and　insensitive　loss　coefficient　of　the　support　vector　regression　(SVR)　model　using　five　swarm　intelligent　optimization　algorithms,　leading　to　the　development　of　five　hybrid　models.　In　addition,　a　statistical　DCS　prediction　equation　using　multiple　linear　regression　techniques　was　developed.　The　performance　of　the　prediction　models　was　comprehensively　evaluated　using　two　error　indexes　and　two　trend　indexes.　A　sensitivity　analysis　based　on　the　cosine　amplitude　method　has　also　been　conducted.　The　results　demonstrate　that　the　proposed　hybrid　SVR-based　models　consistently　provided　accurate　DCS　predictions.　Among　these　models,　the　SVR　model　optimized　with　the　chameleon　swarm　algorithm　exhibited　the　best　performance,　with　metrics　indicating　its　effectiveness,　including　root　mean　square　error　(RMSE)　=　3.9675,　mean　absolute　error　(MAE)　=　2.9673,　coefficient　of　determination　(R2)　=　0.98631,　and　variance　accounted　for　(VAF)　=　98.634.　This　suggests　that　the　chameleon　swarm　algorithm　yielded　the　most　optimal　results　for　enhancing　SVR　models.　Notably,　impact　pressure　and　characteristic　impedance　emerged　as　the　two　most　influential　parameters　in　DCS　prediction.　This　research　is　anticipated　to　serve　as　a　reliable　reference　for　estimating　the　DCS　of　rocks　subjected　to　freeze-thaw　weathering.　©　2023　Institute　of　Rock　and　Soil　Mechanics,　Chinese　Academy　of　Sciences