Machine　scheduling　serves　as　a　vital　function　for　industrial　and　service　operations,　and　uncertainties　always　pose　a　significant　challenge　in　real-world　scheduling　practices.　In　this　paper,　we　propose　to　solve　the　stochastic　machine　scheduling　problems　with　uncertain　processing　times　by　an　improved　prescriptive　tree-based　(IPTB)　model.　Our　approach　includes　a　novel　way　of　combining　historical　processing　time　data　with　current　scheduling　constraints　to　strengthen　the　quality　of　historical　decisions.　We　apply　these　improved　historical　decisions　and　incorporate　an　improved　model　for　calculating　the　optimisation　loss　and　accelerate　the　training　of　our　IPTB　model.　Our　trained　model　can　directly　prescribe　downstream　scheduling　solutions　with　high　robustness　in　the　face　of　uncertainties.　We　evaluate　the　proposed　IPTB　method　on　a　stochastic　parallel　machine　scheduling　problem　originating　from　printed　circuit　board　assembly　lines.　Through　a　series　of　comparative　experiments,　our　findings　demonstrate　the　IPTB　method’s　superior　accuracy　and　robustness,　highlighting　its　resilience　in　noisy　data　environments.　Additionally,　we　interpret　the　model　through　feature　importance　analysis　and　examine　the　model’s　behaviours　under　noisy　conditions.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.