Accurate estimation of Poisson’s ratio is essential for the characterization of coal-bearing gas reservoirs, particularly in thin-bed and low signal-to-noise ratio environments where conventional elastic impedance (EI) inversion suffers from wavelet interference, limited resolution, and reliance on linearization assumptions. To address these limitations, we develop a physics-guided two-branch convolutional neural network (TB-CNN) that directly predicts Poisson’s ratio by jointly integrating EI-inverted P-wave velocity, S-wave velocity, and density with small-, medium-, and large-angle seismic stacks. The first branch provides geologically consistent, physics-informed background trends, while the second branch captures thin-bed-sensitive reflectivity features and amplitude tuning effects. The fused latent representation is explicitly regularized using empirical rock-physics relationships to ensure physical plausibility and enhanced generalization. Field validation on the 8# coal seam of the Ordos Basin demonstrates that the proposed TB-CNN improves vertical resolution, sharpens seam boundary delineation, and better preserves thickness variations compared with EI inversion and a single-branch CNN. Near-well comparisons show higher correlation with log-derived Poisson’s ratio, while lateral slices reveal improved continuity and thin-layer detectability. These results confirm that combining physics-guided stability with data-driven resolution provides a robust and interpretable framework for Poisson’s ratio inversion in thin coal seams and holds promise for broader applications in unconventional gas reservoir prediction.