This　study　introduces　a　quasi-two-stage　single　screw　expander　(QTS-SSE)　designed　to　improve　the　performance　of　organic　Rankine　cycle　(ORC)　systems.　A　predictive　model　specifically　for　the　QTS-SSE　was　developed　using　machine　learning　techniques.　Thermodynamic,　economic,　and　environmental　models　were　developed　to　analyze　the　impact　of　key　adjustable　devices,　including　the　QTS-SSE,　working　fluid　pump,　and　cooling　water　pump,　on　net　power　(Pnet),　net　efficiency　(ηORC),　electricity　production　cost　(EPC),　and　equivalent　CO2　emissions　(ECE).　Results　show　that　the　prediction　error　of　QTS-SSE　model　is　minimal.　The　velocity　expansion　power　reaches　0.56　kW,　which　enhances　the　isentropic　efficiency　of　expander　by　2.48　%.　Both　Pnet　and　ηORC　increased　by　5.31　%　each,　while　EPC　and　ECE　decreased　by　4.25　%　and　5.14　%,　respectively.　The　maximum　efficiency　of　the　working　fluid　pump　was　30　%,　while　the　water　pump　was　62.6　%.　Additionally,　multi-objective　particle　swarm　optimization　was　employed　to　balance　energy,　economic,　and　environmental　performance.　The　innovations　in　this　paper　enrich　the　theoretical　frameworks　of　volumetric　expanders,　broaden　efficient　operating　conditions,　explore　mechanisms　by　which　the　novel　expander　improves　ORC　systems,　and　provide　a　reliable　reference　for　researchers　to　optimize　overall　ORC　system　performance.　©　2025