Deep learning-based methods have made significant progress in image dehazing. However, these methods often falter when applied to real-world hazy images, primarily due to the scarcity of paired real-world data and the limitations of current dehazing feature extractors. Toward these issues, we introduce a novel Physics Embedded Illumination Estimation (PEIE) method for adaptive real-world dehazing. Specifically, (1) we identify the limitations of the widely used Atmospheric Scattering Model and propose a new physical model, the Illumination-Adaptive Scattering Model (IASM), for more accurate illumination representation in hazy imaging; (2) we develop a robust data synthesis pipeline that leverages the physics embedded illumination estimation to generate realistic hazy images; and (3) we design an Illumination-Adaptive Dehazing Unit (IDU) to extract dehazing features consistent with our proposed IASM in the latent space. By integrating the IDU into a U-Net architecture to create IADNet, we achieve significant improvements in dehazing performance through end-to-end training on synthetic data. Extensive experiments validate the superior performance of our PEIE method, significantly surpassing the state-of-the-arts in real-world dehazing.