Two-dimensional　Janus　III-VI　monolayers　and　corresponding　van　der　Waals　(vdW)　heterostructures　present　immense　application　potential　in　the　solar　energy　conversion　areas.　In　this　work,　we　present　material　screening　and　machine　learning　modeling　to　accelerate　the　discovery　of　promising　photocatalytic　and　photovoltaic　candidates　in　Janus　III-VI　vdW　heterostructures.　A　comprehensive　database　with　a　total　of　19926　heterostructures　has　been　proposed　according　to　the　high-throughput　first-principles　calculations.　It　is　highlighted　that　we　develop　an　accurate　machine　learning　model　using　only　atoms　and　bonds　as　descriptors　based　on　the　crystal　graph　convolutional　neural　network　framework.　Besides,　1035　Janus　III-VI　vdW　heterostructures　have　been　screened　out　according　to　the　essential　criteria　of　stability.　Moreover,　we　find　66　and　71　potential　candidates　for　photocatalysis　and　solar　cells,　respectively,　from　further　application-driven　screening.　Interestingly,　the　screened　type-II　SeInAlS/SeGaAlTe　heterostructure　with　a　band　gap　of　1.18　eV　is　highlighted　as　an　internal　electric　field-driven　asymmetrical　photocatalyst.　On　the　other　hand,　the　type-II　Al2STe/Al2SSe　heterostructure　solar　cell　presents　power　conversion　efficiencies　higher　than　21%　both　from　the　microscopic　and　mesoscopic　point　of　views.　We　believe　that　our　study　will　provide　a　feasible　strategy　for　the　design　of　III-VI　monolayers　for　solar　energy　applications.