Polybrominated diphenyl ethers (PBDEs), one of the most commonly used flame retardants, are classified as persistent organic pollutants that pose significant risks to the environment and human health. Therefore, they should be removed from the environment using degradation processes. However, in complex environmental matrices the progress of degradation is challenging to follow. Hence, modelling studies are necessary to understand the fate of PBDEs and develop effective remediation strategies. This study aims to model the anaerobic dehalogenation of PBDEs in sediments and analyze the degradation pathways and rates under various bioremediation scenarios. For this purpose, experimental data from a microcosm study simulating natural attenuation, biostimulation, and bioaugmentation scenarios were utilized. A previously developed anaerobic dehalogenation model (ADM) was enhanced and integrated to create a new model called 'ADM-IE.' ADM-IE has the capability to list all possible dehalogenation pathways for PBDEs, calculate the degradation rate constants for the measured compounds, and estimate the rate constants for those not measured, using machine learning algorithms. As a result, the model performed better in predicting higher-concentration compounds, whereas its accuracy decreased for lower-concentration compounds. It was determined that the position of bromine atoms (ortho, meta, para) played a critical role in dehalogenation pathways. Among the bioremediation scenarios, bioaugmentation generally achieved the highest degradation rates, while biostimulation showed higher rates in some cases. However, certain pathways supported the formation of toxic products, emphasizing the need for caution when applying biostimulation. The model provided an analysis framework for optimizing bioremediation strategies by achieving less harmful degradation products.