This review provides a comprehensive overview of modern experimental and numerical methods for characterizing the sound absorbing properties of dissipative sound-absorbing materials. Experimentally, we summarize both in situ techniques (e.g., pulse reflection, two-microphone, p-u probe, and spatial Fourier transform method) and laboratory methods (e.g., impedance tube, transfer function, and reverberation room methods), discussing their principles and applications. For the numerical methods, we detail the development and refinement of empirical models (e.g., Delany–Bazley, Miki, Komatsu), theoretical models (e.g., Johnson–Champoux–Allard), and computer numerical methods, along with methods for obtaining flow resistivity, including empirical formulas, experimental measurements. Furthermore, we review recent advances in machine learning approaches (e.g., generalized regression neural networks, radial basis function neural networks, and artificial neural networks) for predicting the sound absorption coefficient. This work aims to serve as a methodological reference for the research, development, and performance evaluation of dissipative sound-absorbing materials.