Artificial intelligence (AI) is changing modern bioprocessing by offering new ways to design, monitor, and optimize complex biological systems. Traditional tools such as mechanistic models and Design of Experiment (DoE) often fall short when faced with the nonlinear behaviour and variability that are common in fermentation, cell culture, and purification processes. O, the other hand, Machine Learning (ML), Deep Learning (DL), Evolutionary Algorithms, Reinforcement Learning (RL), and Digital Twins (DT) can uncover patterns, predict outcomes, and guide decision-making with far greater accuracy. This review highlights how these AI approaches are being applied across upstream processing, downstream purification, real-time monitoring, and scale-up and put forwards some industrial case studies, including AI-assisted COVID-19 vaccine development by Pfizer and Moderna, CHO fed-batch optimization with titer

increase of up to 48%, data-driven chromatography prediction with R² value of 0.90, GA-based feeding strategies, and AI based soft sensors for E. coli, that illustrate the practical benefits now seen in industry. The paper also puts forward some major challenges and discusses emerging trends such as autonomous digital twins, multi-omics integration, and IoT-enabled monitoring. Overall, these developments show how AI is steadily becoming a core enabler of faster, more consistent, and more efficient biomanufacturing.