Context:　The　research　of　machine　learning　for　requirements　engineering　(ML4RE)　has　attracted　more　and　more　attention　from　researchers　and　practitioners.　Although　pioneering　research　has　shown　the　potential　of　using　ML　techniques　to　improve　RE　practices,　there　lacks　a　systematic　and　comprehensive　literature　review　in　academia　that　integrates　an　industrial　perspective.　Specifically,　none　of　the　reviews　available　in　ML4RE　have　considered　the　grey　literature,　which　is　primarily　from　practitioner　origin　and　is　more　reflective　of　the　real　issues　and　challenges　faced　in　practice.　Objective:　In　this　paper,　we　conduct　a　systematic　survey　of　academic　publications　in　ML4RE　and　complement　it　with　the　practitioners＇　voices　from　Stack　Overflow　to　complete　a　comprehensive　literature　review.　Our　research　objective　is　to　provide　a　comprehensive　view　of　the　current　research　progress　in　ML4RE,　present　the　main　questions　and　challenges　faced　in　RE　practice,　understand　the　gap　between　research　and　practice,　and　provide　our　insights　into　how　the　RE　academic　domain　can　pragmatically　develop　in　the　future.　Method:　We　systematically　investigated　207　academic　papers　on　ML4RE　from　2010　to　2022,　along　with　375　questions　related　to　RE　practices　on　Stack　Overflow　and　their　corresponding　answers.　Our　analysis　encompassed　their　trends,　focused　RE　activities　and　tasks,　employed　solutions,　and　associated　data.　Finally,　we　conducted　a　joint　analysis,　contrasting　the　outcomes　of　both　parts.　Results:　Based　on　the　statistical　results　from　collected　literature,　we　summarize　an　academic　roadmap　and　analyse　the　disparities,　offering　research　recommendations.　Our　suggestions　include　the　development　of　intelligent　question-answering　assistants　employing　large　language　models,　the　integration　of　machine　learning　into　industrial　tools,　and　the　promotion　of　collaboration　between　academia　and　industry.　Conclusion:　This　study　contributes　by　providing　a　holistic　view　of　ML4RE,　delineating　disparities　between　research　and　practice,　and　proposing　pragmatic　suggestions　to　bridge　the　academia-industry　gap.