The convergence of AI and biotechnology is reshaping the design, construction, testing, and optimization of biological systems. This chapter outlines the current integration of AI into the Design–Build–Test–Learn (DBTL) cycle, supported by biofoundries, standardized data formats, and automation. We examined self-driving laboratory concepts and case studies that demonstrated closed-loop AI-driven experimentation. Advances in protein structure prediction (AlphaFold, RoseTTAFold, and ESM) and generative sequence design (ProteinMPNN and diffusion models) have been reviewed for applications in enzyme engineering and drug discovery. AI-enabled genome editing and breeding, including improvements in guided RNA design, directed evolution, and precision fermentation, are also discussed. In bioprocess engineering, we cover AI-assisted media design and scale-up and process analytical technologies, soft sensors, and digital twins for real-time monitoring and optimization. This chapter also addresses ethical, safety, and governance issues such as biosecurity, dual-use risks, DNA synthesis screening standards, and the transparency of AI models. Through selected examples, we highlight how AI tools accelerate discovery, enhance reproducibility, and expand the scope of feasible biological design. This chapter concludes with perspectives on the remaining challenges, regulatory considerations, and the need for interdisciplinary training to realize the full potential of AI-driven biotechnology.