To　ensure　secure　and　reliable　communication　in　wireless　systems,　authenticating　the　identities　of　numerous　nodes　is　imperative.　Traditional　cryptography-based　authentication　methods　suffer　from　issues　such　as　low　compatibility,　reliability,　and　high　complexity.　Physical-Layer　Authentication　(PLA)　is　emerging　as　a　promising　complement　due　to　its　exploitation　of　unique　properties　in　wireless　environments.　Recently,　Machine　Learning　(ML)-based　PLA　has　gained　attention　for　its　intelligence,　adaptability,　universality,　and　scalability　compared　to　non-ML　approaches.　However,　a　comprehensive　overview　of　state-of-the-art　ML-based　PLA　and　its　foundational　aspects　is　lacking.　This　paper　presents　a　comprehensive　survey　of　characteristics　and　technologies　that　can　be　used　in　the　ML-based　PLA.　We　categorize　existing　ML-based　PLA　schemes　into　two　main　types:　multi-device　identification　and　attack　detection　schemes.　In　deep　learning-based　multi-device　identification　schemes,　Deep　Neural　Networks　are　employed　to　train　models,　avoiding　complex　processing　and　expert　feature　transformation.　Deep　learning-based　multi-device　identification　schemes　are　further　subdivided,　with　schemes　based　on　Convolutional　Neural　Networks　being　extensively　researched.　In　ML-based　attack　detection　schemes,　receivers　utilize　intelligent　ML　techniques　to　set　detection　thresholds　automatically,　eliminating　the　need　for　manual　calculation　or　knowledge　of　channel　models.　ML-based　attack　detection　schemes　are　categorized　into　three　sub-types:　Supervised　Learning,　Unsupervised　Learning,　and　Reinforcement　Learning.　Additionally,　we　summarize　open-source　datasets　used　for　PLA,　encompassing　Radio　Frequency　fingerprints　and　channel　fingerprints.　Finally,　this　paper　outlines　future　research　directions　to　guide　researchers　in　related　fields.　©　2024　Elsevier　Ltd