High-performance　concrete　compressive　and　tensile　strengths　are　essential　in　terms　of　the　assurance　of　structural　performance　and　reliability.　The　research　will　describe　the　effective　estimation　of　such　properties　through　an　artificial　intelligence-based　approach　to　overcome　several　limitations　of　experimental　testing.　For　this　purpose,　a　Light　Gradient　Boosting　model　has　been　developed　and　enhanced　using　four　meta-heuristic　optimization　algorithms:　Dandelion　Optimization,　Runge-Kutta　Optimization,　Seagull　Optimization　Algorithm,　and　Black　Widow　Optimization　Algorithm.　The　LGRDSB　was　an　ensemble　model　that　combined　the　strengths　of　all　four　optimizers.　Among　them,　the　RUN　optimizer　with　the　LGRK　model　emerged　as　the　best,　giving　R-squared　values　of　0.9928　and　0.9914　for　CS　and　TS　predictions,　respectively.　Thus,　the　LGRDSB　model　ensemble　emerged　as　most　robust　and　reliable　to　handle　diverse　datasets,　securing　R-squared　values　greater　than　98%　and　less　than　1%　error　rates.　These　results　highlight　the　performance　of　the　proposed　models　in　predicting　HPC　properties　and　provide　a　realistic　approach　toward　integrating　AI　techniques　into　performance　evaluation　for　HPC.