Integration and Implementation of Machine Learning & Artificial Intelligence in Surface‐Enhanced Raman Spectroscopy

ABSTRACT Surface‐enhanced Raman spectroscopy (SERS) is an analytical technique widely used for molecular identification and trace detection, offering minimal sample preparation, high sensitivity, molecular fingerprinting, and quantitative capability. However, SERS measurements generate large, complex spectral datasets whose interpretation is time‐consuming and requires specialized expertise. The integration of artificial intelligence (AI) and machine learning (ML) provides effective strategies for automated data processing, feature extraction, and pattern recognition, enabling a new generation of intelligent SERS sensing platforms. Unlike previous reviews, which have largely catalogued AI/ML‐SERS applications by domain, this work provides a critical synthesis structured around four contributions: (i) a workflow‐level taxonomy linking SERS data characteristics to algorithm selection; (ii) a quantitative meta‐analysis of approximately 40 representative studies covering the distribution of reported accuracies, the relationship between dataset size and reported performance, and the temporal shift from classical chemometrics to deep learning; (iii) a dedicated assessment of validation pitfalls specific to AI/ML‐SERS, including substrate batch effects, technical‐replicate leakage, spectrum‐level versus sample‐level splitting, and the absence of external validation cohorts; and (iv) a practical reporting checklist for future AI/ML‐SERS studies. By foregrounding methodological rigor rather than enumeration, the review aims to support both SERS practitioners adopting AI/ML and ML researchers entering spectroscopic sensing.