Machine Learning‐Enhanced 3D‐Printed Nanozyme Biosensors for Point‐of‐Use Lactate Detection

ABSTRACT Lactate (LA) is a key biomarker of anaerobic metabolism, hypoxia, and cellular stress, with elevated levels indicating metabolic changes during intense exercise, sepsis, and microbial activity. Despite its broad relevance, real‐time, user‐friendly LA sensing remains challenging. Here, we report a versatile 3D‐printed hydrogel biosensor integrating lactate oxidase (LOx) and ceria nanozyme (nCe) mimetics, coupled with smartphone imaging and machine learning (ML) for robust, matrix‐specific LA detection. The LOx/nCe hydrogel enables single‐step colorimetric sensing, while ML‐based analysis compensates for environmental imaging variations and improves detection accuracy, achieving a detection limit of 46 µ m LA. Synergistic enzymatic and nanozyme catalysis provides molecular recognition and signal amplification, enabling sensitive and selective measurements across diverse sample types. The biosensors exhibit excellent storage stability, maintaining performance for 45 d at room temperature, 6 months at 4°C, and 9 months at −20°C. The 3D‐printing approach enables reproducible and scalable fabrication, while smartphone‐based ML analysis provides real‐time concentration prediction with an R 2 of 0.95. The platform is demonstrated in sweat monitoring and intelligent food packaging applications for milk and seafood freshness. Overall, this work establishes a low‐cost, point‐of‐use LA sensing platform for personalized health tracking, athletic performance monitoring, and intelligent food‐quality assessment.