Machine　learning　method　with　heuristic　optimization　algorithms　is　proposed　to　predict　the　stratum　displacement　induced　by　earth　pressure　balanced　shield　tunneling.　Support　vector　regression　is　used　as　the　machine　learning　method.　Four　heuristic　intelligent　optimization　algorithms,　namely,　genetic　algorithm,　particle　swarm　optimization,　grey　wolf　optimizer　and　sparrow　search　algorithm,　are　applied　to　optimize　the　two　hyperparameters　of　support　vector　regression　model,　namely,　penalty　factor　and　bandwidth　term.　Simulated　annealing　algorithm　is　introduced　to　show　the　necessity　of　using　heuristic　algorithms.　Mean　square　error　of　k-fold　cross　validation　is　considered　as　the　fitness　function　for　optimization　algorithms.　Normalization　method　and　dummy　variables　are　used　for　data　preprocessing.　For　115　samples　from　field　measurement,　92　samples　are　used　as　the　training　set,　and　23　samples　are　used　as　the　test　set.　Three　categories　of　parameters,　namely,　shield　tunneling　parameters,　tunnel　geometrical　parameters　and　stratum　types,　are　used　as　input　parameters　for　the　proposed　method.　Correlations　among　parameters　are　analyzed　by　Pearson　correlation　coefficient.　The　prediction　results　show　that　grey　wolf　optimizer　and　sparrow　search　algorithm　are　suitable　methods　for　determining　hyperparameters　of　support　vector　regression　due　to　higher　accuracy,　efficiency,　and　stability.　©　2022　International　Association　for　Gondwana　Research