Microplastics are increasingly detected in environmental and biological matrices, including water, soil, sediments, air, food, and living organisms. Their widespread occurrence has created a growing need for reliable methods capable of identifying and quantifying different polymer types. Fourier-transform infrared (FTIR) and Raman spectroscopy are widely used for microplastic characterization; however, their performance may be limited when analyzing very small particles, weathered plastics, complex mixtures, or samples containing substantial amounts of organic and inorganic material. Pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS) provides a complementary analytical approach that can address some of these limitations. In Py-GC/MS, polymers are thermally decomposed into characteristic products, which are subsequently separated by gas chromatography and detected by mass spectrometry. The resulting pyrolysis products can serve as diagnostic markers for identifying polymers such as polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET). A major advantage of Py-GC/MS is its ability to provide mass-based quantification of individual polymers, including plastic particles that may be too small for reliable optical or spectroscopic analysis. However, because the method is destructive, it generally does not provide information about particle number, size, shape, or morphology. The reliability of Py-GC/MS can be influenced by sample-matrix composition, sample-preparation procedures, pyrolysis conditions, the selection of diagnostic products, instrumental parameters, and calibration strategies. Organic matter and other matrix components may produce interfering pyrolysis products, complicating polymer identification and quantification. Furthermore, the absence of universally harmonized procedures limits the comparability of results across studies and laboratories. Appropriate sample preparation, matrix-matched or otherwise validated calibration, procedural blanks, reference materials, and comprehensive quality-control measures are therefore essential for obtaining reliable data. This review summarizes the fundamental principles of Py-GC/MS and evaluates its applications to the identification and quantification of microplastics in different environmental matrices. It critically examines analytical challenges associated with sample preparation, polymer-specific marker selection, matrix interferenc