Fiber　reinforced　polymer　(FRP)　composites　are　susceptible　to　material　degradation　when　exposed　to　environmental　effects.　To　predict　the　residual　tensile　strength　and　modulus　of　pultruded　FRP　composites,　an　XGBoost　decision　tree　model　was　developed　in　this　work.　XGBoost　decision　tree,　as　a　machine　learning　technique,　is　able　to　provide　accurate　predictions　for　tabular　dataset　with　a　good　prediction　interpretability.　In　this　work,　the　methodology　of　XGBoost　decision　tree　was　presented　in　detail.　Datasets　for　training　and　testing　included　a　total　of　746　data　points　which　were　collected　from　an　existing　database.　XGBoost　decision　tree　model　predictions　were　cross-validated　with　149　test　data,　and　an　excellent　agreement　was　observed,　showing　R-2　values　of　0.93　and　0.85　for　tensile　strength　and　modulus,　respectively.　In　addition,　attribute　importance　analysis　was　conducted　to　quantitatively　evaluate　the　attributes　pertaining　to　FRP　degradations,　including　exposure　time,　exposure　temperature,　pH　value　of　environment,　fiber　volume　fraction,　plate　thickness,　fiber　type　and　matrix　type.　Exposure　time　and　temperature　were　observed　to　have　the　greatest　impacts　on　residual　tensile　properties.　The　proposed　XGBoost　decision　tree　model　provides　a　new　approach　for　predicting　the　long-term　degradations　of　FRP　composites　subjected　to　environmental　effects.