<p>Digital twin (DT) technology is emerging as a promising approach for predictive analysis of real-world processes on the computer screen and transforming the optimization process. In food fermentation industry, feedstock variability, environmental deviations, as well as microbial dynamics together make it a very rigorous, tedious, and time-consuming process. This is where digital twin technology can change the fate of the industry. In this review paper, the theoretical approach along with its components and technological enablers such as soft sensors, cloud-integration platforms for data analysis and storage, process analytical tools, machine learning, artificial intelligence, Internet of Things, etc. of DT are discussed, which combine together to form the digital twin system. This study emphasizes the opportunities that DTs provide to the food fermentation industries, ranging from real-time monitoring and control of the microbial processes, adaptive control, contamination prediction, evaluating metabolites and by-products concentration to the simulation of the physical fermenter. While the technology provides promising results at the bench-scale, several limitations persist, which have been critically examined, such as biological variability, lack of standardized frameworks and common data platforms for diverse datasets. It also highlights the future prospects of the digital twins in the food fermentation industry by inducing modifications in the system and feeding pilot-scale data, which could pave the way for their incorporation, developing a sustainable and cost-effective technology.</p>