The　mode　of　a　distribution　provides　a　significant　summary.　A　regression　model　with　skew-normal　errors　provides　a　useful　extension　for　traditional　normal　regression　models　when　the　data　involve　asymmetric　outcomes.　Finite　mixture　of　regression　(FMR)　models　are　frequently　used　to　analyze　data　that　arise　from　a　heterogeneous　population.　This　work　develops　a　new　data　analysis　tool　called　finite　mixture　of　mode　regression　(FMMoR)　in　order　to　explore　skewed　data　from　several　subpopulations.　The　main　virtue　of　considering　the　FMMoR　models　for　skewed　data　is　that　this　class　of　models　has　a　nice　hierarchical　representation　which　allows　easy　implementation　of　inferences.　A　productive　clustering　method　via　mode　identification　is　applied　to　select　the　number　of　components.　A　modified　expectation-maximization　algorithm　facilitated　by　the　two-point　step　size　gradient　descent　method　(GDEM),　simultaneously,　is　developed　for　the　inference.　The　proposed　methods　are　evaluated　through　some　simulation　studies　and　illustrated　by　a　real　dataset.