Abstract: Biomanufacturing processes are highly complex and easily perturbed, and achieving online perception of cellular physiological states and critical quality attributes is a prerequisite for building intelligent factories. Centered on the main line of “sensors–data–models–systems,” this paper systematically reviews recent advances in intelligent sensing technologies for biomanufacturing processes. First, it summarizes the working principles and engineering deployment strategies of multiple types of sensors, including physicochemical, spectroscopic, biosensors, and wireless/disposable sensors, and analyzes the statistical characteristics and storage architectures of multi-source, heterogeneous, multi-scale process data. Second, from the three aspects of data preprocessing, feature extraction and dimensionality reduction, and multi-source data fusion, it abstracts typical algorithmic frameworks, provides unified mathematical descriptions for filtering, representation learning, and multimodal modeling. Furthermore, it focuses on the applications of soft sensing, digital twins, and neural-network-based modeling in state estimation, virtual measurements, and closed-loop control, and analyzes the key roles of edge computing, model compression, and explainable artificial intelligence in ensuring real-time performance and decision transparency. Compared with existing review articles that mainly focus on soft sensors, digital twins for bioprocesses, or AI applications, this work takes “intelligent sensing” as the core concept linking sensor physics, data fusion, and model construction. It proposes a structured framework for intelligent sensing in biomanufacturing from four dimensions—sensing layer, data layer, model layer, and system layer—and, in combination with representative industrial scenarios, discusses critical issues for engineering implementation and future development directions, providing methodological guidance for the design and upgrading of biomanufacturing process monitoring systems.