The　stochastic　subspace　identification　(SSI)　technique　is　widely　adopted　for　operational　modal　analysis　of　structural　systems.　Regardless　of　the　specific　implementation,　its　workflow　basically　consists　of　four　main　steps:　definition　of　the　control　parameters　governing　the　SSI　algorithm;　estimation　of　the　system　poles　and　construction　of　the　stabilization　diagram　(SD);　interpretation　of　the　SD;　quantification　of　the　confidence　level　associated　to　the　modal　results.　The　definition　of　the　control　parameters　ruling　the　modal　identification　via　SSI　algorithms　play　a　crucial　role.　Manual　selection　procedures　and　rules-of-thumb　have　been　largely　employed　for　this　task,　but　they　are　unsuitable　for　reliable　automatic　applications.　Therefore,　a　new　paradigm　for　automatic　operational　modal　analysis　is　presented　in　this　work.　The　proposed　approach　is　named　intelligent　automatic　operational　modal　analysis　(i-AOMA)　method　and　relies　on　the　effective　integration　between　the　SSI　algorithm　and　a　machine　learning　technique.　Initially,　quasi-random　samples　of　the　control　parameters　for　the　SSI　algorithm　are　generated.　Once　the　SSI　algorithm　is　performed　for　each　sample,　the　corresponding　SDs　are　processed　to　prepare　a　database　for　training　the　intelligent　core　of　the　i-AOMA　method.　This　is　a　machine　learning　technique　that　drives　intelligently　the　selection　of　the　control　parameters　for　the　next　applications　of　the　SSI　algorithm,　which　is　performed　until　a　convergence　criterion　is　fulfilled.　At　the　end,　the　uncertainty　level　about　the　modal　estimates　due　to　the　variability　of　the　control　parameters　is　assessed.　The　potential　of　the　proposed　approach　is　demonstrated　through　the　identification　of　a　large　structure.