Current trends in machine learning for surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) is being transformed by the widespread adoption of artificial intelligence across the full methodological spectrum. Conventional machine learning is routinely applied for robust baselines and rapid deployment. Deep learning with convolutional networks, recurrent and transformer architectures, and self-supervised objectives is increasingly used to learn invariant spectral representations from minimally processed data. Generative models (variational, adversarial, diffusion) are being employed for augmentation, denoising, and simulation-to-real transfer, while large language models are leveraged for metadata curation, protocol extraction, and retrieval-augmented decision support. Through these advances, SERS analysis has become more convenient, scalable, and automatable, enabling streamlined applications in medicine, agriculture, food quality assurance, environmental monitoring, and process control. Despite this progress, substantial challenges still remain. Data scarcity persists, characterized by limited sample sizes, heterogeneous acquisition protocols, sparse labels, and restricted public benchmarks, which together constrain generalization and hinder fair comparison. Model explainability also requires improvement, with a need for chemically faithful attributions, standardized reporting of evidential spectra, and rigorous robustness checks to build trust in safety-critical decisions. In this review, current methodologies are surveyed, practical guidelines are summarized, and a path forward is outlined that prioritizes community datasets compliant with Findability, Accessibility, Interoperability, and Reuse (FAIR) principles, transparent evaluation suites, and interpretable, uncertainty-aware models capable of reliable deployment across laboratories and devices.