Hydrothermal　co-liquefaction　(co-HTL)　of　different　feedstocks　has　received　much　research　attention,　not　only　because　its　significant　importance　in　real　industrial　applications,　but　also　due　to　the　potential　synergy　in　biocrude　yield　by　tuning　mixed　feedstock＇s　biochemical　composition　and　reaction　conditions.　Although　some　attempts　have　been　made　to　search　for　the　synergy　from　co-liquefying　various　feedstocks,　these　processes　were　remarkably　time　and　labor　consuming,　and　often　with　low　rate　of　success.　Therefore,　this　study　for　the　first　time　employed　machine　learning　algorithms　to　mine　the　synergistic　effect　in　co-HTL.　Started　with　single　task　prediction,　three　machine　learning　algorithms,　including　Adaboost,　Gradient　Boosting　Regression　and　Random　Forest,　were　trained　and　tested　for　predicting　co-HTL　biocrude　yield　and　relative　co-liquefaction　effect　(CE).　It　was　found　that　their　prediction　performances　were　favorable　over　traditional　mathematical　equations,　in　which　Gradient　Boosting　Regression　exhibited　the　best　performance　for　co-HTL　biocrude　yield　prediction　(training　and　testing　R2　of　0.976　and　0.812　respectively),　and　Adaboost　better　estimated　relative　CE.　Feature　importance　analysis　further　revealed　that　co-HTL　biocrude　yield　was　mainly　influenced　by　the　reaction　temperature,　but　relative　CE　was　closely　related　to　mixed　feedstock＇s　lipid　and　carbohydrate　content,　implying　that　the　synergism/antagonism　from　co-HTL　was　more　dependent　on　the　biochemical　composition　of　mixed　feedstock　than　reaction　conditions.　Multitask　predictions,　estimating　biocrude　yield　and　relative　CE　simultaneously　that　are　usually　required　in　real　co-HTL　practices,　suggested　Adaboost　was　the　most　satisfying　algorithm　(training　R2　of　0.922)　among　studied　ones.　An　optimal　relative　CE　of　22.07　%　along　with　36.31　wt%　(daf)　biocrude　yield　could　be　obtained　when　the　mixed　feedstock　contained　42.93　%　protein,　50.49　%　carbohydrate,　6.58　%　lipid　at　a　temperature　of　320　°C,　which　were　in　well　agreement　with　experimental　results　from　co-HTL　of　biomass　model　components.　A　mini　application　software　(exe.　file　including　machine　learning　algorithm)　was　also　developed　for　quick　estimation　of　synergy　and　co-HTL　biocrude　yield　by　simply　inputting　mixed　feedstock＇s　biochemical　composition　and　reaction　conditions,　showing　promising　potential　for　academic　and　industrial　practices　to　mine　the　co-HTL　synergy　and　design　processes　efficiently.　©　2022　Elsevier　Ltd