In　this　paper,　we　introduce　a　family　of　distributions　known　as　generalized　scale　mixtures　of　asymmetric　generalized　normal　distributions　(GSMAGN),　characterized　by　remarkable　flexibility　in　shape.　We　propose　a　novel　finite　mixture　model　based　on　this　distribution　family,　offering　an　effective　tool　for　modeling　intricate　data　featuring　skewness,　heavy　tails,　and　multi-modality.　To　facilitate　parameter　estimation　for　this　model,　we　devise　an　ECM-PLA　ensemble　algorithm　that　combines　the　Profile　Likelihood　Approach　(PLA)　with　the　classical　Expectation　Conditional　Maximization　(ECM)　algorithm.　By　incorporating　analytical　expressions　in　the　E-step　and　manageable　computations　in　the　M-step,　this　approach　significantly　enhances　computational　speed　and　overall　efficiency.　Furthermore,　we　persent　the　closed-form　expressions　for　the　observed　information　matrix,　which　serves　as　an　approximation　for　the　asymptotic　covariance　matrix　of　the　maximum　likelihood　estimates.　Additionally,　we　expound　upon　the　corresponding　consistency　characteristics　inherent　to　this　particular　mixture　model.　The　applicability　of　the　proposed　model　is　elucidated　through　several　simulation　studies　and　practical　datasets.