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Polybrominated diphenyl ethers (PBDEs) are halogenated organic compounds that are among the major pollutants of water, and there is an urgent need for their removal. This work compared the application of two techniques, i.e., photocatalytic reaction (PCR) and photolysis (PL), for 2,2,4,4- tetrabromodiphenyl ether (BDE-47) degradation. Although a limited degradation of BDE-47 was observed by photolysis (LED/N₂), photocatalytic oxidation by using TiO₂/LED/N₂ proved to be effective in the degradation of BDE-47. The use of a photocatalyst enhanced the extent of BDE-47 degradation by around 10% at optimum conditions in anaerobic systems. Experimental results were systematically validated through modeling with three new and powerful Machine Learning (ML) approaches, including Gradient Boosted Decision Tree (GBDT), Artificial Neural Network (ANN), and Symbolic Regression (SBR). Four statistical criteria (Coefficient of Determination (R²), Root Mean Square Error (RMSE), Average Relative Error (ARER), and Absolute Error (ABER)) were calculated for model validation. Among the applied models, the developed GBDT was the desirable model for predicting the remaining concentration (Ce) of BDE-47 for both processes. Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD) results confirmed that BDE-47 mineralization required additional time than its degradation in both PCR and PL systems. The kinetic study demonstrated that BDE-47 degradation for both processes followed the pseudo-first-order form of the Langmuir-Hinshelwood (L-H) model. More importantly, the calculated electrical energy consumption of photolysis was shown to be ten percent higher than that for photocatalysis, possibly due to the higher irradiation time required in direct photolysis, which in turn increases electricity consumption. This study is useful in proposing a feasible and promising treatment process for the degradation of BDE-47.