<p>This study investigates the enhanced degradation of Alizarin Red S (ARS) from aqueous solutions using an ultrasound-assisted persulfate (PS/US) system, combining experimental design and artificial intelligence-driven optimization. ARS, a persistent anthraquinone dye, poses significant environmental and health risks due to its toxic and non-biodegradable nature. Traditional wastewater treatment methods often fail to degrade such dyes effectively, necessitating advanced oxidation processes (AOPs). Here, persulfate (PS) activated by ultrasound (US) was employed to generate highly reactive sulfate (SO₄^·−) and hydroxyl (^·OH) radicals, achieving efficient ARS degradation. A Box-Behnken Design (BBD) coupled with Response Surface Methodology (RSM) was utilized to optimize key operational parameters, including contact time (50–70 min), PS concentration (0.1–0.7 mM), initial ARS concentration (5.0–15 mg/L), US frequency (20–60 kHz), and pH (7.0–11). The quadratic model demonstrated high predictive accuracy (R² = 0.96), with contact time and PS concentration identified as the most influential factors. Artificial intelligence techniques, including RANSAC regression and Monte Carlo simulation, were applied to model and optimize the process, achieving a degradation efficiency of 89.32% under optimal conditions (57.5 min, 0.43 mM PS, 8.5 mg/L ARS, 20.2 kHz, pH 8.1). Kinetic analysis revealed pseudo-first-order degradation (rate constants: 0.0196–0.0279 min^-1), with sulfate radicals contributing dominantly (67.2%) to ARS oxidation. Scavenger experiments and EPR spectroscopy confirmed the synergistic role of SO₄^·− and ^·OH radicals. The presence of inorganic anions (e.g., carbonate, bicarbonate) reduced efficiency, highlighting the need for pre-treatment in complex matrices. This study presents a robust, scalable approach for ARS degradation, integrating sonochemistry, sulfate radical-based AOPs, and AI-driven optimization to advance sustainable wastewater treatment technologies.</p>