The use of Biopolymer-based sensors has emerged as a new, environmentally sustainable and biocompatible approach for detecting various biochemical markers. With their versatile physicochemical interfaces, they have enabled the immobilization of proteins, nucleic acids, enzymes, and nanomaterials, enabling selective, real-time tracking of physiological processes. However traditional biosensors are characterized by drift, nonuniform transduction, limited multiplexing and stability, especially in dynamic clinical settings. The systemic approach to overcome these limitations is the integration of machine learning (ML), which extracts latent patterns, refines the analyte classification, reduces sensor noise, and provides predictive diagnostics. This review summarises the latest advances in biopolymer materials, such as polysaccharides, proteins, and nucleic acid-based architectures. It assesses their use in sensing, tissue engineering, drug delivery, and wound monitoring. In addition, it evaluates the most significant ML paradigms, including supervised, unsupervised, deep learning, and reinforcement learning, in the biosensing workflow, starting with raw data collection and decision-making diagnostics. Compared with the existing literature, this review culminates in a novel conceptual framework that systematically maps biopolymer material properties to specific sensor signal distortions and for the first time, links these phenomena to the most appropriate machine learning strategies for signal correction and diagnostic interpretation. Lastly, the review provides an overview of current challenges and a solid framework for future research on high-performance, sustainable, and clinically usable innovative diagnostic systems.
Manik et al. (2026) studied this question.