Microplastics can enter agro-food systems via multiple pathways, including agricultural production, environmental transport, post-harvest handling, processing, packaging, and distribution. Major sources can be attributed to the residue, weathering, fragmentation, and secondary breakdown of agricultural plastics, such as mulching films, greenhouse covers, irrigation tapes, pipes, nets, and food-contact materials. Particles can be redistributed through irrigation water, surface runoff, soil dust resuspension, atmospheric deposition, and the agricultural application of compost or sewage sludge. Additional contamination can also cause from abrasion, shedding, and migration of processing equipment, filtration media, packaging materials, and storage interfaces. Together, these pathways can pose a great challenge to the multi-source, multi-stage, and cross-media exposure pattern, leading to difficult-to-source identification, risk interpretation, and food safety. This review aims to focus on microplastic monitoring, assessment, and process control in the agro-food chain. Major source categories and contamination pathways were summarized in crop production, animal production, and processing environments, and then examined the reported occurrence in plant-derived foods, animal-derived foods, and processed products. Available studies showed that microplastics were widely detected in vegetables, fruits, cereals, shellfish, fish, meat products, bottled water, salt, sugar, honey, beer, and liquid milk. But reported concentrations varied substantially over studies, leading to orders of magnitude. Such heterogeneity was strongly influenced by commodity differences and regional context, method factors, including particle-size thresholds, reporting metrics with particle number or mass, pretreatment intensity, recovery correction, blank control, confirmation criteria, and sampling contexts, such as washing, peeling, retail cutting, cooking, and packaging. Direct comparison was limited for the quantitative interpretation, especially when trophic transfer coexisted with process-related contamination. Therefore, current occurrence data were more useful to identify high-concern commodities, high-concern links, and major uncertainty sources, compared with the cross-study ranking under heterogeneous analytical conditions. Analytical challenges were further highlighted under complex agro-food matrices rich in lipids, proteins, polysaccharides, pigments, and inorganic particulates, where microplastics often occurred at trace levels over broad size ranges. Practical bottlenecks were closely related to matrix removal, polymer preservation, particle loss control, and procedural contamination prevention. Pretreatment strategies, including chemical digestion, enzymatic digestion, density separation, and membrane filtration, were compared from the perspective of matrix applicability, polymer compatibility, recovery performance, and quality-control requirements. Chemical digestion provided effective organic matter removal, but caused damage to polymers under strong acidic or oxidative conditions. Enzymatic digestion offered milder treatment and better polymer preservation but remained constrained by cost, duration, and reagent background. Density separation was used for enrichment efficiency, but it was required for the selection of separation media and recovery validation, especially for small particles and high-density polymers. Membrane filtration functioned as a concentration step as a critical interface affecting optical imaging, verification, and background interference. Verification workflows were reviewed over microscopy and fluorescence imaging, vibrational spectroscopy, thermal analysis, and emerging high-throughput platforms. Among them, microscopy and fluorescence staining supported rapid morphological screening, but visual identification alone remained vulnerable to false positives and operator subjectivity. Micro-FTIR and micro-Raman spectroscopy provided for chemical identification central to polymer at the particle level, yet they were constrained by throughput, diffraction limits, fluorescence interference, and spectral-library dependence. Thermal evaluations, such as Py-GC/MS and TED-GC/MS, were mass-based quantification of visually undetectable particles, but their performance in food matrices remained sensitive to marker interference, calibration strategy, and background decomposition products. Emerging techniques, including laser direct infrared imaging, optical photothermal infrared spectroscopy, atomic force microscopy infrared spectroscopy, hyperspectral imaging, surface-enhanced Raman scattering, and portable sensing systems, were expected to improve throughput, particle-size coverage, and field-oriented monitoring, although their broader application still depended on scenario adaptation and quality. Artificial intelligence was reviewed as an auxiliary tool for high-throughput screening, spectral interpretation, and multimodal data integration. Image-based models were used to improve particle localization, counting, and morphological characterization in fluorescence and microscopic datasets. While machine learning and deep-learning were supported spectral denoising, feature extraction, data matching, and multimodal fusion over imaging, FTIR, and Raman data. Their practical value depended on transparent training datasets, reproducible preprocessing pipelines, external validation over matrices and devices, drift monitoring, reject-option strategies, and alignment with conventional analytical indicators, such as recovery, blank correction, repeatability, detection limits, and particle-size-specific performance. As such, artificial intelligence was better supported by standardized monitoring and data interpretation rather than a substitute for chemical verification. Furthermore, a flux accounting perspective was introduced to link endpoint measurements with process control. Input and output fluxes of the individual unit were defined to quantify the removal efficiency and net introduction. This framework was used to identify critical control points and then evaluate mitigation options. Source reduction, process interception, and terminal were selected as complementary controls. Priority measures included the life cycle of agricultural plastics, fragmentation-prone inputs, water purification, control of sludge and compost application, high-friction processing interfaces, and standardized sampling, pretreatment, reporting, and quality-control requirements. Safety thresholds were required for microplastics in agro-food products, covering precautionary and prioritizing comparable data generation, critical control points, and controllable exposure reduction over the full chain. Overall, the microplastics in agro-food systems can be expected to move from isolated endpoints toward integrated monitoring, process evaluation, and process-oriented detection.