<p>Bioreactors serve as the core of modern bioprocessing, enabling precise control over microbial, enzymatic, and mammalian cell cultures. Yet, their complex dynamics, nonlinear behaviour, and multivariable interactions often limit the effectiveness of traditional modelling and control methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer data-driven solutions to enhance modelling accuracy, adaptive control, and real-time optimization. This systematic review investigates the application of AI/ML in bioreactor engineering by examining key bioreactor types, data modalities, algorithmic strategies, control frameworks, and hybrid models. Adhering to PRISMA 2020 guidelines, 58 peer-reviewed articles were selected from five databases—Scopus, IEEE Xplore, ScienceDirect, Scispace, and Google Scholar—using structured Boolean queries. The review highlights the use of algorithms such as artificial neural networks (ANN), support vector machines (SVM), random forests (RF), long short-term memory (LSTM), and reinforcement learning (RL) across batch, fed-batch, continuous, and perfusion systems. Applications include soft sensor development, state estimation, fault diagnosis, and closed-loop control using time-series, sensor, and multi-omics data. Hybrid approaches that fuse mechanistic and AI/ML models—such as grey-box systems, physics-informed neural networks (PINNs), and digital twins—are gaining traction due to their improved generalization and interpretability. Despite these advancements, challenges remain regarding model validation, scalability, and regulatory approval. The review identifies future research directions, including explainable AI, cyber-physical integration, transfer learning, and multi-omics fusion. Overall, AI/ML integration marks a paradigm shift toward intelligent, adaptive, and efficient bioprocess control.</p>