This paper examines the role of Generative Adversarial Networks (GANs) and deep learning techniques in graphic production and creative processes related to architecture. The study discusses how AI systems trained on large labeled image datasets can recognize, classify, and generate visual content, highlighting the interaction between generative and discriminative networks as a mechanism for iterative learning and image synthesis.
Through a critical review of current applications—such as DALL·E, MidJourney, AICAN, and The Next Rembrandt—the research analyzes the potential of GAN-based systems to produce architectural representations, artistic images, and design proposals. It also explores their integration into architectural workflows, including parametric design, generative design, simulation tools, and BIM-based environments. Results indicate that AI can support creative exploration, automate repetitive processes, and generate alternative design scenarios, while remaining dependent on training data and constrained by limited contextual understanding. The study concludes that GAN-based AI represents a powerful assistive tool rather than a replacement for architects, with future developments likely to enhance hybrid human–machine design processes.