Biomanufacturing is undergoing a profound paradigm shift from traditional experience-driven methods to innovative data-driven approaches, with artificial intelligence (AI) serving as a central enabling force. This article systematically reviews breakthroughs and applications of AI across key domains of biomanufacturing. In biological component design, generative AI facilitates “from-scratch” innovation for creating DNA regulatory elements, signal peptides, and enzymes, while continuously refining functionality through intelligent “design-build-test-learn” closed-loop systems. This shift enables researchers to move beyond imitation of natural systems toward truly rational and programmable biological design. In metabolic network reconstruction, multi-scale metabolic models combined with advanced deep learning frameworks like AlphaGEM enable dynamic simulation of complex metabolic behaviors, cross-species generalization, and exploration of previously uncharted biological functions. These computational advancements are particularly significant for engineering non-model organisms with industrial relevance, where traditional characterization methods are often inadequate. Within cell-free synthesis systems, AI converges with microfluidic and automation platforms to establish high-throughput, intelligent, and self-optimizing protein production pipelines, effectively decoupling biological synthesis from the constraints of cellular viability and regulation. This approach enables rapid prototyping of biomolecules that would be difficult or impossible to produce using conventional cellular systems. For process optimization and control, AI drives the evolution toward rationally designed, dynamically optimized, and autonomously regulated production through predictive modeling, real-time sensor integration, and adaptive control strategies. These systems continuously learn from process data to maximize yield, minimize variability, and ensure consistent product quality while reducing resource consumption and operational costs. While AI has substantially accelerated biomanufacturing research and development (R&D) and enhanced production efficiency, several challenges remain. These include incomplete understanding of underlying biological mechanisms, scarcity of high-quality annotated datasets, limited model interpretability (often described as the “black box” problem), complexities in multi-objective optimization, and stringent real-time operational demands in industrial settings. Looking forward, deeper integration of AI with synthetic biology, robotic automation, and multi-omics data analytics will propel biomanufacturing toward a programmable, intelligent, and sustainable next-generation industrial ecosystem. Future advancements are expected to include more sophisticated physics-informed neural networks that incorporate fundamental biological principles, federated learning approaches to leverage distributed data while maintaining privacy, and the development of digital twins for entire bioprocesses. This convergence is poised to deliver a key technological engine for the green transformation of critical sectors such as pharmaceuticals, chemicals, and energy. Ultimately, the synergy between AI and biotechnology promises to establish a more efficient, resilient, and environmentally conscious bioeconomy capable of addressing global challenges in health, materials, and sustainable production.
Jiang et al. (2026) studied this question.