Urban traffic prediction is hindered by heterogeneous sensor configurations and complex wave-like traffic flow dynamics (e.g., congestion waves, stop-and-go oscillations) that conventional neural networks struggle to model effectively. We propose WaveFSL, a novel wave physics-inspired few-shot learning framework that unifies wave interference theory with adaptive neural modules to address these challenges. WaveFSL integrates five key components: (1) a Dynamic Input Projection (DIP) for handling variable-dimensional input through learnable dimension-aware projection; (2) a Traffic Wave Generator (TWG) synthesising parameterised wave components (with amplitude, effective frequency, and phase); (3) a Wave-Constrained Interference (WCI) explicitly modelling congestion propagation via coupled wave superposition; (4) a Wave-Aware Spectral Attention (WASA) for multiscale spectral analysis through resonance scoring and frequency-band decomposition; and (5) a Few-Shot Adaptation with optimal kernels (FSAK) enabling rapid domain adaption and transfer via prototype-based conditioning. By unifying wave interference theory with adaptive neural learning, WaveFSL achieves state-of-the-art performance across four real-world datasets, outperforms baselines (3.2–8.5% MAE reduction), generalises with only 5–10 labelled samples per city, and requires no retraining. It enables interpretable, deployable traffic forecasting under realistic constraints. Code is available at: https://github.com/afofanah/WaveFSL.