High-strength,　lightweight　alloys　are　highly　desired　in　the　aerospace,　automotive　and　marine　industries.　The　optimization　of　such　alloys　is　a　multifaceted　process,　characterized　by　the　consideration　of　diverse　objectives　and　constraints.　Various　optimization　methodologies　exist,　spanning　from　intricate　first-principle　approaches　to　the　application　of　sophisticated　machine　learning　algorithms.　These　algorithms　might　incorporate　input　features　encompassing　elemental　composition,　microstructural　attributes,　and　thermodynamic　properties　to　enhance　prediction　accuracies.　In　this　work,　we　aim　to　streamline　this　complexity　by　employing　solely　the　alloy＇s　elemental　composition　as　the　input　feature　for　the　machine　learning　algorithm,　improving　the　hardness　while　reducing　the　density　of　the　alloy.　We　have　curated　a　comprehensive　database　comprising　544　multi-principal　element　alloys　and　developed　a　robust　surrogate　model　based　on　these　compositions.　This　composition-driven　model　is　subsequently　coupled　with　principal　component　analysis　(PCA)　to　facilitate　the　selection　process.　Remarkably,　through　a　mere　three　iterations　involving　14　samples,　we　successfully　identified　an　alloy　with　an　effective　specific　hardness　surpassing　the　training　database　maximum　by　8.6　%.　The　proposed　composition-driven　machine　learning　delineates　a　simplified　approach　for　conducting　optimization　across　multiple　target　material　properties.