Silk fibroin is a potential substance in a variety of sectors, including biomedicine, textiles, and cosmetics, because of its well-known biocompatibility, mechanical strength, and biodegradability. To maximize its usage in these applications, artificial intelligence techniques must be used to comprehend and forecast its behavior. Machine learning stands out as the most broadly used AI technique because of its capacity to evaluate massive datasets and identify significant patterns. Researchers can forecast important characteristics of silk fibroin, like its mechanical strength under various circumstances, its rate of deterioration, and its interactions with other materials or biological systems, by using machine learning techniques. This chapter examines current developments in modeling and predicting the behavior of silk fibroin using artificial intelligence (AI), with a focus on machine learning. It emphasizes how AI has the potential to speed up the creation of latest silk-based materials and applications. The incorporation of artificial intelligence not only advances our basic comprehension of silk fibroin but also opens up new avenues for creative applications in the field of biomaterials science and beyond.