A Systematic Review of Artificial Intelligence and Machine Learning Techniques for Microplastic Detection and Analysis

Microplastics are a pervasive contaminant of aquatic, terrestrial, and atmospheric systems, with accumulating evidence of ecological harm and human exposure. Conventional workflows, manual microscopy, FTIR, and Raman spectroscopy, are labor-intensive and operator-dependent, motivating artificial intelligence (AI) and machine learning (ML) as scalable alternatives. Following PRISMA guidelines, five databases were searched; 936 records were screened and 113 primary studies met the eligibility criteria, analyzed through dual-reviewer extraction across seven dimensions. Contrary to the assumption that deep learning dominates, classical and chemometric estimators (46.0% of studies) were at least as prevalent as deep learning (36.3%), with support-vector machines the single most used family (35.4%). Method choice tracked input modality rather than recency: chemometric classifiers such as SVM and PLS dominate spectroscopic data (FTIR, Raman; 53.1% of studies), whereas deep learning concentrates in the image-based minority, where representation learning outperforms manual feature engineering. Detection/identification remained the principal task (65.5%), although 19.5% addressed predictive modeling of sorption, toxicity, distribution, and remediation. Characterization coverage was uneven: origin and polymer type were reported in 88.5% and 72.6% of studies, but size and shape in only 50.4% and 28.3%. Reported accuracies, often exceeding 90%, derive from heterogeneous, non-comparable datasets. Future progress depends on open benchmarks, standardized reporting, and explainable methods.