Sericulture, a vital agro-based industry, has long relied on labor-intensive and experience-driven practices, making it vulnerable to climatic stressors, pest outbreaks, and inconsistent productivity. The integration of Artificial Intelligence (AI) into sericulture offers transformative solutions across the value chain—from mulberry cultivation and silkworm rearing to cocoon grading and silk processing. This review synthesizes recent advances (2015–2025) in AI technologies including computer vision, machine learning, deep learning, and IoT systems. Applications discussed include disease detection with over 99% accuracy, yield forecasting with <10% error, 96-fold improvements in cocoon processing speed, and AI-driven environmental control systems. Case studies from countries like Bangladesh demonstrate significant productivity gains, while sustainability considerations underscore AI's role in reducing pesticide use and enhancing precision agriculture. Ethical concerns around nanomaterials and data privacy are also addressed, alongside challenges such as infrastructure limitations, standardization gaps, and human capacity needs. The review concludes with future research directions in multimodal fusion, digital twins, and inclusive innovation strategies. Overall, AI holds immense potential to make sericulture smarter, more efficient, and sustainable.
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