This　paper　proposes　a　Wasserstein　distributionally　robust　learning　(WDRL)　model　to　predict　the　production　cycle　time　of　highly　mixed　printed　circuit　board　(PCB)　orders　on　multiple　production　lines.　The　PCB　production　cycle　time　is　essential　for　optimizing　production　plans.　However,　the　design　of　the　PCB,　production　line　configuration,　order　combinations,　and　personnel　factors　make　the　prediction　of　the　PCB　production　cycle　time　difficult.　In　addition,　practical　production　situations　with　significant　disturbances　in　feature　data　make　traditional　prediction　models　inaccurate,　especially　when　there　is　new　data.　Therefore,　we　establishe　a　WDRL　model,　derive　a　tight　upper　bound　for　the　expected　loss　function,　and　reformulate　a　tractable　equivalent　model　based　on　the　bound.　To　demonstrate　the　effectiveness　of　this　method,　we　collected　data　related　to　surface　mounted　technology　(SMT)　production　lines　from　a　leading　global　display　manufacturer　for　our　computational　experiments.　In　addition,　we　also　designed　experiments　with　perturbations　in　the　training　and　testing　datasets　to　verify　the　WDRL　model＇s　ability　to　handle　perturbations.　This　proposed　method　has　also　been　compared　with　other　machine　learning　methods,　such　as　the　support　vector　regression　combined　with　symbiotic　organism　search,　decision　tree,　and　kernel　extreme　learning　machine,　among　others.　Experimental　results　indicate　that　the　WDRL　model　can　make　robust　predictions　of　PCB　cycle　time,　which　helps　to　effectively　plan　production　capacity　in　uncertain　situations　and　avoid　overproduction　or　underproduction.　Finally,　we　implement　the　WDRL　model　for　the　actual　SMT　production　to　predict　the　production　cycle　time　and　set　it　as　the　target　for　production.　We　observed　a　98–103　%　achievement　rate　in　the　last　20　months　since　the　implementation　in　September　2022.　©　2024　The　Authors