Iterative design–build–test–learn (DBTL) cycles are routinely performed during microbial strain development. This useful approach integrates computational strain design, genetic engineering, fermentation testing, and omics analysis to reveal and resolve production bottlenecks. However, the DBTL may enter involution, in which the numerous engineering cycles generate large amount of information and constructs without leading to breakthroughs. To avoid this problem, machine learning (ML) can be a promising yet not developed solution to multiscale modeling and process optimization. This review discusses the recent advances in ML applications, focusing on integrative metabolic models and knowledge engineering for guiding metabolic engineering and fermentation optimization. The ML-based strain development can eventually improve DBTL cycles to facilitate moving synthetic strains from laboratories to industries.