From Nature to Neutral Networks: AI-Driven Biomimetic Optimization in Architectural Design and Fabrication

The integration of biomimetics and artificial intelligence (AI) in architecture is reshaping the foundations of computational design. This paper provides a comprehensive review of the current research trends and applications that combine AI-driven modeling with biologically inspired principles to optimize architectural forms, material efficiency, and fabrication processes. By examining recent studies from Q1–Q2 journals (2019–2025), the paper identifies five primary “interfaces” through which AI expands the field of biomimetic design: biological pattern recognition, structural optimization, generative morphogenesis, resource management, and adaptive fabrication. The paper highlights the transition from conventional simulation-based design toward iterative, data-driven workflows integrating machine learning (ML), deep generative models, and reinforcement learning. The findings demonstrate that AI not only serves as a generative tool but also as a learning mechanism capable of translating biological intelligence into architectural logic. The paper concludes by proposing a methodological and educational framework for AI-driven biomimetic optimization, emphasizing the emergence of Artificial Intelligence in Architectural Design (AIAD) as a paradigm shift in architectural education and research. This convergence of biology, algorithms, and material systems is defining a new, adaptive approach to sustainable and intelligent architecture.