Microplastics (MPs) are newly identified pollutants in wastewater and various locations, posing a concern for the environment due to their potential hazards to both the environment and human health. Even though traditional vibrational spectroscopies such as Fourier-transform infrared (FTIR) and Raman mapping have enabled polymer-specific identifications of MPs, they remain limited by diffraction and require very long acquisition times, as well as by the presence of complex wastewater matrices. Recent advances, including microfluidic-based separation and hyperspectral imaging (HSI), have enhanced the detection of submicron and nanoplastic fractions, improved throughput, and enabled real-time monitoring. HSI enables the rapid acquisition of spatially resolved spectral cubes across the entire filter, while microfluidics provides size- and density-based separation using laminar flow, acoustic, and dielectrophoretic forces. Artificial intelligence (AI) and machine learning (ML) are combined to accelerate spectral categorization, enabling the precise identification of polymer types, even in the presence of fluorescence, biofilms, or weathered particles. Convolutional neural networks and transfer learning models trained on different polymer types demonstrate more than 90% accuracy in classifying various types of wastewater spectra. Hybrid workflows combining FTIR, Raman, HSI, and AI-inspired data processing could offer automated, high-throughput, and consistent monitoring of MPs in wastewater. These new methodologies enable environmental risk assessment to be conducted more accurately, and wastewater and regulatory management plans to be developed using more reliable information by filling existing gaps in methods.