Microplastics (<5 mm) are now found in oceans, rivers, drinking water and human blood, yet routine monitoring is limited because reference methods such as Fourier-transform infrared (FTIR) and Raman spectroscopy are laboratory-bound, slow and costly. Microwave resonator sensing offers a low-cost, non-optical alternative: the large permittivity contrast between water (~80) and common polymers (~2–3.5) means suspended particles shift a planar resonator\'s resonance frequency, quality factor and transmission/reflection response. This narrative review covers sensing theory, resonator design, microfluidic sample han-dling, machine-learning interpretation (K-nearest neighbours, convolutional neural networks) and portable, wireless integration. Reviewed studies report resonance shifts of ~100 to >500 MHz, a theoretical resolution of ~100 ppm, a limit of detection of 48.72 ng/mL (microfluidic-CNN seawater system), response times below 250 ms, and prediction accuracies of 96–98 % within ±0.01 mg/mL. However, most studies use few polymers, filtered or artificial matrices, laboratory network analysers, non-standard concentration units and random data splits, and few address temperature or salinity compensation or long-term field operation; the seawater study\'s quoted detection limit and tolerance also need independent confirmation. We contribute a com-parative analysis of representative studies, a conceptual architecture for field-deployable, IoT-connected monitoring, and a prior-itised research agenda.