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Here, we introduce a data\u2010driven framework that employs a generative deep neural network to create ultrathin, multilayer epsilon\u2010near\u2010zero absorbers with record\u2010breaking broadband performance. Within this framework, the neural network learns a generative distribution over high\u2010performing multilayer designs, optimizing the refractive index, layer arrangement, and thickness for maximal broadband absorption. Guided by AI, we experimentally demonstrate an optimized photonic structure that achieves near\u2010perfect light absorption (exceeding 98%) across more than 1000\u00a0nm bandwidth in the near\u2010infrared range, while remaining under 160\u00a0nm thick. Our approach demonstrates the power of integrating advanced computational algorithms with state\u2010of\u2010the\u2010art fabrication technology, offering a scalable route to high\u2010efficiency nanophotonic devices for target applications. Altogether, this work establishes a generalizable strategy for algorithm\u2010driven photonic design with implications across energy, sensing, and integrated optics.<\/jats:p>","DOI":"10.1002\/advs.77966","type":"journal-article","created":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T18:00:31Z","timestamp":1790532031000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generative AI\u2010Enabled Discovery and Experimental Demonstration of Ultra\u2010Broadband Epsilon\u2010Near\u2010Zero Perfect Absorbers"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6910-6215","authenticated-orcid":false,"given":"David","family":"Dang","sequence":"first","affiliation":[{"name":"Department of Physics and Astronomy University of California  Irvine California USA"},{"name":"Center for Integrated Nanotechnologies Los Alamos National Laboratory  Los Alamos New Mexico USA"},{"name":"Beckman Laser Institute 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