The　isentropic　efficiency　of　the　working　fluid　pump　has　a　significant　impact　on　the　overall　performance　of　the　organic　Rankine　cycle　(ORC)　system.　Based　on　machine　learning,　this　paper　proposes　an　experimental　data-driven　isentropic　efficiency　prediction　model.　Meanwhile,　S-fold　cross　validation　algorithm　and　smoothing　factor　circulation　screening　technology　are　used　to　improve　the　predictive　ability　of　the　model.　The　prediction　accuracy　of　the　optimized　model　and　the　unoptimized　model　are　compared　with　each　other.　The　influence　of　several　operating　parameters　on　isentropic　efficiency　are　analyzed.　In　addition,　with　intelligent　algorithm,　the　boundary　values　of　operating　parameters　are　determined.　The　genetic　algorithm　(GA)　and　particle　swarm　optimization　(PSO)　are　combined　into　a　GA-PSO　hybrid　algorithm.　Subsequently,　the　hybrid　algorithm　is　integrated　with　the　machine　learning　model　to　predict　and　optimize　the　isentropic　efficiency　under　full　operating　conditions.　The　highest　isentropic　efficiency　reaches　up　to　58.73%.　The　prediction　and　optimization　of　the　isentropic　efficiency　of　multistage　centrifugal　pump　under　full　operating　conditions　provides　not　only　a　useful　guidance　on　assuming　pump　efficiencies　in　theoretical　analysis,　but　also　a　meaningful　reference　for　obtaining　the　optimum　overall　operating　performance　of　the　ORC　system.　(c)　2021　Elsevier　Ltd.　All　rights　reserved.