Support　vector　regression　(SVR)　with　sparrow　search　algorithm　(SSA)　is　developed　as　the　machine　learning　(ML)　model　to　predict　maximum　surface　settlement　smax　caused　by　tunneling.　A　novel　method　for　calibrating　boundary　conditions　of　analytical　solution　is　proposed,　where　the　maximum　surface　settlement　derived　by　the　analytical　method　is　equal　to　smax　predicted　by　SSA-SVR　method.　The　elastic　analytical　solution　for　stratum　displacement　of　a　shallow　tunnel　is　presented　by　the　complex　variable　method,　when　the　calibrated　nonuniform　displacement　function　is　applied　as　the　tunnel　displacement　boundary　condition.　The　proposed　analytical　solution-machine　learning　(AM)　method　can　predict　the　stratum　displacement　field　prior　to　the　tunnel　excavation.　Seventythree　tunnel　engineering　cases　are　employed　to　verify　the　rationality　of　the　proposed　SSA-SVR　method　in　predicting　smax.　The　value　of　R2　in　the　training　and　test　process　is　0.894　and　0.877,　respectively.　Taking　Heathrow　Express　Trial　Tunnel　as　an　example,　the　potential　of　the　proposed　AM　method　in　predicting　stratum　displacement　is　presented　where　the　influence　of　cohesion　strength,　internal　friction　angle,　Young＇s　elastic　modulus　of　stratum,　tunnel　radius　and　depth　are　considered.　The　proposed　AM　method　can　well　predict　the　stratum　surface　settlement　trough　curve,　vertical　and　horizontal　displacement　at　different　positions.