Electrospinning Technology, Machine Learning, and Control Approaches: A Review

Electrospinning is a versatile technique for producing micro‐ and nanoscale fibers, offering vast potential to address critical market demands, particularly in biomedical engineering. However, the industrial adoption of electrospinning as a manufacturing technology faces significant hurdles, notably in achieving precise control over fiber properties and ensuring reproducibility and scalability. These challenges directly impact its viability for creating advanced biomedical products. Bridging the gap between material properties, end‐user requirements, and process parameters is essential for unlocking the full potential of electrospinning. This work provides a comprehensive review of electrospinning modalities, operational factors, and modeling techniques, emphasizing their role in optimizing the electrospinning process. The use of control strategies and machine learning methods is explored, showcasing their potential to enhance the electrospinning performance. This review highlights the connection between product properties and performance in electrospinning, as well as the necessary conditions for its use in biomedical applications. In addition, the review identifies gaps and unexplored areas, offering a roadmap for future innovation in fiber fabrication. By emphasizing the synergy between intelligent process design and biomedical applications, this work lays the groundwork for advancements, positioning electrospinning as a cornerstone of next‐generation manufacturing technologies.