Nalbant, Kemal GökhanAydın, SevgiUyanık, Şevval2025-03-092025-03-0920231305-77662587-2451https://doi.org/10.26468/trakyasobed.1297678https://search.trdizin.gov.tr/tr/yayin/detay/1219381https://hdl.handle.net/20.500.12662/4832The application of machine learning, deep learning, and artificial intelligence is ubiquitous across various domains. The Generative Adversarial Network (GAN) is considered a remarkable deep learning architecture among its peers. Provided that an ample quantity of data samples is fed to the GAN model, it is feasible to generate novel samples of the same data category. This architectural design served as the foundation for numerous programs. The GAN has emerged as a prominent deep learning framework that has had a significant impact on the field of digital art. This article primarily focuses on elucidating the fundamental aspects of GAN, including its definition, operational mechanism, classification, practical implementations, and correlation with digital art. Simultaneously, inquiries pertaining to the definition of digital art, its practical implementations, and its correlation with the metaverse and digital marketing are being scrutinized.eninfo:eu-repo/semantics/openAccessArtificial IntelligenceDigital ArtDigital Marketing.Generative Adversarial Network (GAN)Metaverse MarketingArticle10.26468/trakyasobed.12976783962375121938125