This　study　utilized　available　oral　acute　toxicity　data　in　Rat　and　Mouse　for　polychlorinated　persistent　organic　pollutants　(PC-POPs)　to　construct　data　fusion-driven　machine　learning　(ML)　global　models.　Based　on　atom-　centered　fragments　(ACFs),　the　collected　high-throughput　data　overcame　the　applicability　limitations,　enabling　accurate　toxicity　prediction　for　a　wide　range　of　PC-POPs　series　compounds　using　only　single　models.　The　data　variances　in　the　Rat　training　and　test　sets　were　1.52　and　1.34,　respectively,　while　for　the　Mouse,　the　values　were　1.48　and　1.36,　respectively.　Genetic　algorithm　(GA)　was　used　to　build　multiple　linear　regression　(MLR)　models　and　pre-screen　descriptors,　addressing　the　＂black-box＂　problem　prevalent　in　ML　and　enhancing　model　interpretability.　The　best　ML　models　for　Rat　and　Mouse　achieved　approximately　90　%　prediction　reliability　for　over　100,000　true　untested　compounds.　Ultimately,　a　warning　list　of　highly　toxic　compounds　for　eight　cat-　egories　of　polychlorinated　atom-centered　fragments　(PCACFs)　was　generated　based　on　the　prediction　results.　The　analysis　of　descriptors　revealed　that　dioxin　analogs　generally　exhibited　higher　toxicity,　because　the　heteroatoms　and　ring　systems　increased　structural　complexity　and　formed　larger　conjugated　systems,　contributing　to　greater　oral　acute　toxicity.　The　present　study　provides　valuable　insights　for　guiding　the　subsequent　in　vivo　tests,　envi-　ronmental　risk　assessment　and　the　improvement　of　global　governance　system　of　pollutants.