Microplastics (MPs, < 5 mm) and nanoplastics (NPs, < 1 μm) have emerged as pervasive environmental contaminants due to the extensive use and continuous release of plastic materials. Despite growing awareness of their ecological and health risks, achieving rapid, accurate, and multidimensional detection of these particles remains a formidable analytical challenge. This review identifies the major analytical challenges in detecting NPs within environmental matrices and synthesizes recent advances across mass spectrometry, spectroscopy, and optical/electron microscopy, supported by machine learning and microfluidics. These emerging techniques enable faster, more accurate detection of smaller NPs and improve discrimination among polymer types and coexisting materials. We introduce a functionality and performance-oriented framework that classifies detection strategies into four capability domains reflecting future requirements: (1) high-throughput quantification of particle concentration, (2) accurate polymer identification, (3) spatially resolved imaging of particle distributions, and (4) in situ multimodal analysis. By transcending traditional instrument-based classifications, this framework enables meaningful cross-method comparisons under shared analytical objectives and underscores a broader transition in the field toward multimodal integration, advanced data analytics, and in situ characterization. We synthesize the strengths of emerging analytical strategies, propose quantitative performance benchmarks, and provide guidance for their effective translation into real-world environmental monitoring.