Fermentation, a crucial biological process used in various industries, is undergoing significant transformation through the integration of advanced technologies, such as metagenomics and machine learning. Traditionally, fermentation has been utilized in food and beverage production, as well as in the synthesis of biofuels, chemicals, and pharmaceuticals. However, the advent of metagenomics allows for the comprehensive study of microbial communities involved in fermentation, enabling the exploration of microbial diversity and metabolic functions in their natural environments. Machine learning, on the other hand, has revolutionized the analysis of large-scale datasets, offering predictive modeling and optimization of fermentation processes. By combining metagenomics with machine learning, new insights into microbial dynamics, metabolic pathways, and the formation of bioproducts can be achieved, leading to enhanced efficiency and product quality. This chapter explores the fusion of these two powerful tools, focusing on their applications in fermentation science and biotechnology. We review the fundamental principles of metagenomics and machine learning, their integration in industrial and artisanal fermentation, and their potential to drive sustainable bioprocesses. The future of fermentation science is increasingly shaped by these technologies, offering promising solutions for improving yields, optimizing processes, and addressing global challenges such as food security and environmental sustainability.