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Artificial intelligence may catalyze adoption by addressing the optimization complexity hindering practical deployment, particularly as 6G targets demanding applications requiring efficient group-oriented transmission. Following PRISMA guidelines, this survey reviews the 5G MBS architecture and analyzes 23 studies applying AI to multicast and broadcast optimization. Surveyed works demonstrate computational complexity reductions from\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$O(N^3)$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>O<\/mml:mi>\n                            <mml:mo>(<\/mml:mo>\n                            <mml:msup>\n                              <mml:mi>N<\/mml:mi>\n                              <mml:mn>3<\/mml:mn>\n                            <\/mml:msup>\n                            <mml:mo>)<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    to\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$O(N^2)$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>O<\/mml:mi>\n                            <mml:mo>(<\/mml:mo>\n                            <mml:msup>\n                              <mml:mi>N<\/mml:mi>\n                              <mml:mn>2<\/mml:mn>\n                            <\/mml:msup>\n                            <mml:mo>)<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , throughput gains of 18\u201350%, and resource savings up to 33%. Deep reinforcement learning variants dominate resource allocation and scheduling, while unsupervised clustering methods address multicast group formation and federated learning enables privacy-preserving optimization across distributed deployments. We organize findings across six areas: physical layer intelligence, RAN slicing and scheduling, multicast group formation and routing, RIS-assisted transmission, D2D-assisted multicast, and end-to-end optimization. We identify underexplored areas, non-terrestrial networks, ISAC integration, graph neural networks, and foundation models, and provide a research roadmap addressing standardization gaps and deployment barriers.\n                  <\/jats:p>","DOI":"10.1007\/s11235-026-01407-1","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T17:48:08Z","timestamp":1769622488000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Survey on AI technologies aiding multicast and broadcast services in 5G and 6G"],"prefix":"10.1007","volume":"89","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7508-8797","authenticated-orcid":false,"given":"Marcell","family":"Szab\u00f3","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1045-9205","authenticated-orcid":false,"given":"L\u00e1szl\u00f3","family":"Toka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"1407_CR1","unstructured":"3GPP: (2023). 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