Human　exposure　to　per-　and　polyfluoroalkyl　substances　(PFASs)　has　received　considerable　attention,　particularly　in　pregnant　women　because　of　their　dramatic　changes　in　physiological　status　and　dietary　patterns.　Predicting　internal　PFAS　exposure　in　pregnant　women,　based　on　external　and　relevant　parameters,　has　not　been　investigated.　Here,　machine　learning　(ML)　models　were　developed　to　predict　the　serum　concentrations　of　PFOA　and　PFOS　in　a　large　population　of　588　pregnant　participants.　Dietary　exposure　characteristics,　demographic　parameters,　and　in　particular,　serum　fatty　acid　(FA)　data　were　used　for　the　model　development.　The　fitting　results　showed　that　the　inclusion　of　FAs　as　covariates　significantly　improved　the　performance　of　the　ML　models,　with　the　random　forest　(RF)　model　having　the　best　predictive　performance　for　PFOA　(R2　=　0.33,　MAE　=　1.51　ng/mL,　and　RMSE　=　1.89　ng/mL)　and　PFOS　(R2　=　0.12,　MAE　=　2.65　ng/mL,　and　RMSE　=　3.37　ng/mL).　The　feature　importance　analysis　revealed　that　serum　FAs　greatly　affected　PFOA　concentration　in　the　pregnant　women,　with　saturated　FAs　being　associated　with　decreased　PFOA　levels　and　unsaturated　FAs　with　increased　levels.　Comparison　with　one-compartment　pharmacokinetic　model　further　demonstrated　the　advantage　of　the　ML　models　in　predicting　PFAS　exposure　in　pregnant　women.　Our　models　correlate　for　the　first　time　blood　chemical　concentrations　with　human　FA　status　using　ML,　introducing　a　novel　perspective　on　predicting　PFAS　levels　in　pregnant　women.　This　study　provides　valuable　insights　concerning　internal　exposure　of　PFASs　generated　from　external　exposure,　and　contributes　to　risk　assessment　and　management　in　pregnant　populations.　©　2024　The　Author(s)