AI‐Assisted Topological Photonic Crystals: Design Methods, Applications, Challenges and Opportunities

ABSTRACT Deep learning is becoming a core driving force for advances in topological photonics. This work provides how deep learning helps address key challenges in topological photonics and discusses future research directions. We summarize and discuss recent AI‐assisted methods for topological photonic crystals (TPCs) and their representative application pathways. The main TPC architectures and recent progress on representative topological states in two‐ and three‐dimensional photonic crystals are re‐examined, with an emphasis on how deep learning can improve design efficiency, enable robustness evaluation, and help bridge the gap between numerical models and as‐fabricated structures. We analyze the key bottlenecks that currently limit practical deployment, such as fabrication tolerance, loss, thermal drift, and topology‐aware diagnosis. We discuss emerging opportunities for learning‐ enabled topological photonic platforms and outline how AI may accelerate the transition of TPCs from proof‐of‐concept physics to deployable photonic technologies.