Artificial-Intelligence-Driven Marine Biosensors: From Smart Materials Design to Intelligent Ocean Monitoring

Marine environmental monitoring is critical for ecological preservation, climate regulation, and the sustainability of the blue economy. However, traditional marine biosensors face severe bottlenecks when deployed in situ, driven by the overwhelming matrix effects of high salinity, aggressive biofouling, and highly fluctuating physical parameters across variable ocean scales. Recently, the convergence of marine biosensing technologies and artificial intelligence (AI) has been proposed as a transformative route toward transcending these long-standing barriers. This narrative review critically surveys recent advances in AI-driven marine biosensors, organizing the evidence through an explicit classification of genuine biosensors, biomimetic sensors, conventional chemical sensors, and AI-assisted environmental monitoring systems, and labeling each technology according to its demonstrated validation environment (seawater, freshwater, laboratory, or proposed integration). We first examine how machine learning and deep learning architectures accelerate the inverse design and optimization of recognition elements and functional nanomaterials. Next, we evaluate core AI algorithms dedicated to signal preprocessing, multi-component deconvolution, and nonlinear quantitative regression, which collectively enable robust data interpretation amidst intense marine noise. Furthermore, we highlight cutting-edge applications of these intelligent sensing platforms in tracking heavy-metal pollution, ecological biosecurity threats, and emerging contaminants, followed by an analysis of their systemic integration onto autonomous marine assets coupled with IoT edge-computing and adaptive anti-biofouling frameworks. Finally, we discuss current challenges—including data scarcity, model interpretability (“black-box” limitations), and domain generalizability—and offer a task-prioritized roadmap linking each research priority to sensing tasks and validation endpoints for the realization of globally networked, self-calibrating, intelligent ocean-monitoring systems.