This study investigates a three-dimensional electrooxidation (3D-EO) process utilizing steel slag as a particle electrode for the treatment of mature landfill leachate. First, having been used as a particle electrode the steel slag were characterized by X-ray diffraction (XRD), X-ray fluorescence (XRF), Brunauer–Emmett–Teller (BET), energy-dispersive X-ray spectroscopy (EDS), and Fourier transform infrared spectroscopy (FTIR) analyses. Then, the performance of the conventional two-dimensional electrooxidation (2D-EO) process was compared with that of the 3D-EO process incorporating the particle electrode. While the chemical oxygen demand (COD) removal efficiency was 36.7% in the 2D-EO process, it was 50.4% in the 3D-EO process. Five different anode materials were tested in each process, where Ti/IrO₂ was determined as the optimum anode for both. The process operating parameters were modeled utilizing machine learning (ML) algorithms. Among the evaluated models, the XGBoost algorithm demonstrated the highest predictive accuracy, yielding high R² values coupled with low mean absolute error (MAE) and root mean square error (RMSE) values. The optimal operating parameters were identified as follows: initial pH = 5, particle electrode dosage = 2 g/L, applied current = 2 A, and reaction time = 120 min. The removal efficiencies for COD, UV₂₅₄, and total organic carbon (TOC) at the optimal conditions reached 83.0%, 85.3%, and 51.8%, respectively, requiring a specific energy consumption of 84.4 kWh/kg COD. The 3D-EO process reduced the inert COD fraction from 80.0% to 65.5%, improving both the biochemical oxygen demand/chemical oxygen demand (BOD₅/COD) ratio from 0.1 to 0.4 and the soluble COD fraction from 83.1% to 95.2%. Phytotoxicity assessments indicated that the treated effluent required a 75% dilution to reach a safe, non-toxic threshold (GI > 70%). Overall, the 3D-EO process emerges as a promising and highly effective technology for mature landfill leachate treatment, successfully complemented by the Extreme Gradient Boosting (XGBoost) algorithm.