Fibrous networks are critical structural motifs underpinning numerous biological and engineering materials. Their complex mechanics, governed by fiber properties and topological architecture, therefore require advanced modeling and optimization strategies. This review presents a comprehensive synthesis of recent advances in artificial intelligence (AI)–assisted design, characterization, and optimization of fibrous networks. We explore how deep generative models enable the creation of ordered and disordered architectures with tailored properties, how machine learning facilitates structure–property prediction across multiple physical fields and spatial dimensions, and how reinforcement learning accelerates performance-driven topological optimization. Emphasis is placed on the integration of multi-scale data, physics-informed learning, and explainable AI to enhance design fidelity and interpretability. We conclude by outlining future opportunities for autonomous material systems, including closed-loop discovery platforms and multi-physics integration, positioning AI as a transformative force in fibrous materials innovation.