This　paper　presents　a　symbiotic　organism　search　(SOS)　algorithm　based　support　vector　regression　(SVR)　model　for　predicting　the　production　throughput　in　a　high-mix　and　low-volume　setting.　A　case　study　of　the　printed　circuit　broad　(PCB)　throughput　in　surface　mount　technology　(SMT)　production　lines　is　conducted.　The　accurate　throughput　estimation　of　PCB　order　is　essential　to　optimize　the　SMT　production　schedule,　which　is　more　critical　in　high-mix　and　low-volume　environment.　However,　it　is　difficult　to　estimate　the　throughput　due　to　the　flexibility,　complexity,　and　uncertainty　of　the　PCB　assembly　process.　Therefore,　the　SVR　model,　one　of　the　most　efficient　machine　learning　tools,　is　proposed　to　solve　this　problem.　To　improve　the　performance　of　SVR　model,　the　SOS　algorithm　is　applied　to　optimize　SVR　parameters,　which　include　the　penalty,　the　width　of　kernel　function,　and　the　loss　function.　The　training　and　testing　data　sets　are　collected　from　a　local　electronic　manufacturer.　Compared　with　the　industrial　solution　and　classic　SVR,　the　proposed　model　shows　its　efficiency.