Development in bioprocess engineering has been revolutionized with the integration of metabolomics and multi-omics approaches, CRISPR/Cas technology, and artificial intelligence (AI). CRISPR/Cas and metabolomics have together played a prime role in the optimization of bioprocesses through genetic engineering followed by omics to reveal both metabolic mechanisms and bottlenecks. The integration of CRISPR-based genome editing technology increases the efficiency of precision engineering of microbial resources through targeted knockouts, pathway enhancement, and modulating pathway metabolism regulation toward product yield enhancement. Genetic engineering followed by a metabolomics or multi-omics approach with a combination of genomics, transcriptomics, and proteomics with metabolomics enables real-time metabolic flux analysis and enhances product yields of industrial enzymes and biofuels. This provides a comprehensive idea of the microbial systems and guides data-driven manufacturing processes. Meanwhile, AI and machine learning are transforming both the analysis of omics data as well as bioprocess development with predictive models, automated experimental layout design, and control that adaptively optimize the fermentation process, achieving resource efficiency, time, and effort minimization. Collectively, all these technologies and advancements are shifting the transition toward a more sustainable bioeconomy. This mostly includes waste valorization toward resource recovery, carbon-neutral production, and multifunctional energy-saving bioprocesses. Moreover, this integrated approach provides more economically accessible and eco-friendly options toward the sustainable development goals.