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Abstract Bamboo culm has been widely used in engineering for its high strength, lightweight, and low cost. Its outermost epidermis is a smooth and dense layer that contains cellulose, silica particles, and stomata and acts as a water and mechanical barrier. Recent experimental studies have shown that the layer has a higher mechanical strength than other inside regions. Still, the mechanism is unclear, especially for how the low silica concentration (<10%) can effectively reinforce the layer and prevent the inner fibers from splitting. Here, theoretical analysis is combined with experimental imaging and 3D printing to investigate the effect of the distribution of silica particles on composite mechanics. The anisotropic partial distribution function of silica particles in bamboo skin yields higher toughness (>10%) than randomly distributed particles. A generative artificial intelligence (AI) model inspired by bamboo epidermis is developed to generate particle‐reinforced composites. Besides the visual similarity, it is found that the samples by the generative model show failure processes and fracture toughness identical to the actual bamboo epidermis. This work reveals the micromechanics of the bamboo epidermis. It illustrates that generative AI can help design bio‐inspired composites of a complex structure that cannot be uniformly represented by a simple building block or optimized around local boundaries. It expands the design space of particle‐reinforced composites for enhanced toughness modulus, offering advantages in industries where mechanical reliability is critical.