From Task-Specific Learning to Network-Native Intelligence: A Comprehensive Review of Machine Learning and Artificial Intelligence in Modern Networks

From Task-Specific Learning to Network-Native Intelligence: A Comprehensive Review of Machine Learning and Artificial Intelligence in Modern Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review From Task-Specific Learning to Network-Native Intelligence: A Comprehensive Review of Machine Learning and Artificial Intelligence in Modern Networks Yutong Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9299363/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Machine learning (ML) and artificial intelligence (AI) are no longer peripheral optimization tools for networking; they are becoming integral to how modern networks are measured, controlled, secured, and evolved. Yet the literature remains fragmented. Existing surveys usually focus on one sub-domain at a time—for example encrypted traffic analysis, data-center networking, routing, edge intelligence, or 6G—and therefore under-emphasize the deeper shift from task-specific models to network-native intelligence. This review synthesizes recent literature from roughly 2020 to early 2026, with emphasis on the 2021–2025 wave, and organizes the field through four coupled axes: network lifecycle, deployment scope, learning paradigm, and operational constraints. We examine how supervised, self-supervised, graph-based, reinforcement, federated, generative, and foundation-model approaches have been used for traffic analysis, anomaly and intrusion detection, routing and congestion control, resource orchestration , edge/cloud/data-center optimization, and AI-native mobile/6G systems. We then compare representative studies along data assumptions, generalization behavior, online adaptability, interpretability, systems cost, and reproducibility. Our central argument is that the next phase of AI for networking is not simply “more powerful models” but closed-loop, network-native intelligence: systems that unify perception, reasoning, decision, verification, and actuation under realistic constraints such as privacy, energy, latency, safety, and cross-domain inter-operability. Based on this synthesis, we identify the main review gap in current literature: the lack of a unified, deployment-aware, lifecycle-centric perspective that spans from packet/flow analytics to autonomous network operation and emerging foundation models. We conclude with a concrete research agenda covering trustworthy online learning, digital twins, synthetic data, domain-adapted 1 foundation models, multi-agent control, and sustainable AI for communication networks. machine learning for networking artificial intelligence for networks network management traffic analysis routing congestion control network security edge intelligence data center networking 6G large language models foundation models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 02 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9299363","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":619534054,"identity":"867459c9-aa88-4e13-9f32-4793ec19df7d","order_by":0,"name":"Yutong Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBCDBCBmfIDgs+FTzAzXwmwApkjRwiZBlBaDG/kHHxf8qsvjn91+rfLnj8MM8u1nDBg+lB1m4J/dgENLMrPxzD62Yok7Z8pu8yQcZjA4k2PAOOPcYQaJOwdwaWGT5u3hSWy4kZN2mwGkRYLHgJm3DcRIwKdFInE+UEvhD6AW+RlALX8JaeH5YZC44Ub6MQaQwxhuALUw4tEieeaxsTFvQ0Lixhs5zNI8aek8BmfSCg72nEvnkbiBXQvf8cSHj3n+1CXOu5H+8OMPG2s5+fbDGx/8KLOW45+BXYsCKFQY20BMHgMwCSIOwBjYgHwDiPwDItgf4FI0CkbBKBgFIxwAAD6HX2YPeGQOAAAAAElFTkSuQmCC","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-04-02 07:09:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9299363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9299363/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960052,"identity":"70a15e5d-40d8-4ab3-9443-0160981fe824","added_by":"auto","created_at":"2026-04-15 09:18:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":351796,"visible":true,"origin":"","legend":"","description":"","filename":"FromTaskSpecificLearningtoNetworkNativeIntelligenceAComprehensiveReviewofMachineLearningandArtificialIntelligenceinModernNetworks.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9299363/v1_covered_367842b4-8987-4e0f-bfd5-28732e3e7b1a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Task-Specific Learning to Network-Native Intelligence: A Comprehensive Review of Machine Learning and Artificial Intelligence in Modern Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-networks","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Networks](https://link.springer.com/journal/44354)","snPcode":"44354","submissionUrl":"https://submission.springernature.com/new-submission/44354/3","title":"Discover Networks","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"machine learning for networking, artificial intelligence for networks, network management, traffic analysis, routing, congestion control, network security, edge intelligence, data center networking, 6G, large language models, foundation models","lastPublishedDoi":"10.21203/rs.3.rs-9299363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9299363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Machine learning (ML) and artificial intelligence (AI) are no longer peripheral optimization tools for networking; they are becoming integral to how modern networks are measured, controlled, secured, and evolved. 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