With　the　progress　of　information　technology,　large　amounts　of　asymmetric,　leptokurtic,　and　heavy-tailed　data　are　arising　in　various　fields,　such　as　finance,　engineering,　genetics,　and　medicine.　It　is　very　challenging　to　model　those　kinds　of　data,　especially　for　extremely　skewed　data,　accompanied　by　very　high　kurtosis　or　heavy　tails.　In　this　article,　we　propose　a　class　of　novel　skewed　generalized　t　distribution　(SkeGTD)　as　a　scale　mixture　of　skewed　generalized　normal.　The　proposed　SkeGTD　has　excellent　adaptiveness　to　various　data,　because　of　its　capability　of　allowing　for　a　large　range　of　skewness　and　kurtosis　and　its　compatibility　of　the　separated　location,　scale,　skewness,　and　shape　parameters.　We　investigate　some　important　properties　of　this　family　of　distributions.　The　maximum　likelihood　estimation,　L-moments　estimation,　and　two-step　estimation　for　the　SkeGTD　are　explored.　To　illustrate　the　usefulness　of　the　proposed　methodology,　we　present　simulation　studies　and　analyze　two　real　datasets.　©　2024　Taylor　&　Francis　Group,　LLC.