In　this　paper,　a　novel　skewed　generalized　normal　distribution　(NSGN),　which　encompasses　several　well-known　distributions,　is　a　two-component　mixture　model　based　on　the　generalized　normal　distribution.　The　NSGN　distribution　offers　flexibility　in　modeling　skewed　data　characterized　by　high　kurtosis　and　strong　skewness,　making　it　highly　applicable　in　finance,　medicine,　and　engineering.　The　primary　characteristics　and　properties　of　this　new　distribution　are　introduced,　and　the　explicit　expressions　for　the　moments　of　order　statistics　are　provided　using　recursive　relations.　The　maximum　likelihood　estimation　based　on　a　profile　likelihood　approach,　L-moments　estimation,　and　a　two-step　estimation　method　combining　Bayesian　posterior　maximum　estimation　with　moment　estimates　are　presented　to　estimate　the　four　parameters　of　NSGN　distribution.　A　simulation　study　compares　the　performance　of　these　methods　across　different　sample　sizes　and　different　parameters.　The　appropriateness　of　the　proposed　distribution　has　been　tested　by　comparing　it　with　several　skewed　distributions.　Additionally,　applications　to　real　datasets　showcase　the　beneficial　properties　of　the　NSGN　for　applied　statistical　research.