Four　hybrid　intelligent　methods　are　developed　to　predict　the　maximum　ground　surface　settlement　(S-max)　induced　by　shallow　underground　excavation　method　(SUEM).　Particle　swarm　optimization　(PSO)　algorithm　with　k-fold　cross　validation　is　used　to　determine　the　optimal　hyperparameters　or　random　parameters　in　the　four　machine　learning　(ML)　methods,　namely　that,　back-propagation　neural　network　(BPNN),　extreme　learning　machine　(ELM),　support　vector　regression　(SVR)　and　random　forest　(RF).　100　field　engineering　samples　are　collected　from　published　papers.　In　each　data　sample,　the　effect　of　stratum　mechanical　conditions,　tunnel　geometric　parameters　and　construction　parameters　on　S-max　is　considered.　Correlation　laws　among　parameters　are　investigated　through　Pearson　correlation　coefficient,　data　distribution　histogram　and　correlation　confidence　ellipse.　The　performance　of　four　PSO-based　ML　methods　is　comprehensively　compared　by　fitness　function,　time　cost　and　prediction　accuracy　in　the　training　and　test　processes.　PSO-RF　outperforms　PSO-SVR,　PSO-ELM　and　PSO-BPNN　in　the　prediction　accuracy　for　S-max　owing　to　larger　R-2,　smaller　MAE　and　RMSE.　Calculation　time　that　the　optimal　hyperparameters　are　determined　is　the　fastest　for　PSO-RF,　and　PSO-ELM　has　the　smallest　fitness　function.　The　prediction　performance　of　PSO-RF　method　for　construction　parameters　is　also　discussed.　This　work　can　provide　theoretical　guidance　for　design　and　construction　of　SUEM.