This study employed response surface methodology (RSM), artificial neural networks (ANN), and computational fluid dynamics (CFD) to optimize the mass fraction of total anthocyanins and redness index from pericarp blood orange waste (PBOW). A total of five independent variables were examined: ultrasonic power (ranging from 100 to 300 W), ultrasonic time (5 to 30 min), agitation speed (80 and 160 rpm), ethanol concentration (0 to 50%), and the solid-to-solvent ratio (0.25 to 0.75). The ANN method effectively forecast the experimental data, allowing for a precise model of the nonlinear relationships between extraction parameters and anthocyanin mass fraction and redness index. Consequently, these findings demonstrated that ultrasonic power and ethanol concentration were the most influential independent variables affecting anthocyanin mass fraction and redness index. The analysis revealed that ANN model achieved a high coefficient of determination (R2 = 0.99451), surpassing the predictive accuracy of both RSM and the CFD approaches. The optimal conditions, as determined using the RSM-ANN model, consisted of 270 W of ultrasonic power, 20.30 min of ultrasonic time, 140 rpm agitation speed, an ethanol concentration of 37.5%, and a solid to liquid ratio of 0.375. Additionally, the economic evaluation provided an estimate of the anthocyanin production from orange pericarp wastes. Thus, the combination of these simulated approaches substantially improved the efficiency and yield of anthocyanin extraction from pericarp blood orange waste, resulting in a practical and environmentally friendly method for utilizing these agricultural by-products.