Abstract: Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelligent forging of components. Using the die forging of AA2014 aluminum alloy as a case study, a machine-learning-assisted method for digital modeling of the forging force and autonomous control in response to forging parameter disturbances was proposed. First, finite element simulations of the forging processes were conducted under varying friction factors, die temperatures, billet temperatures, and forging velocities, and the sample data, including process parameters and forging force under different forging strokes, were gathered. Prediction models for the forging force were established using the support vector regression algorithm. The prediction error of F_f, that is, the forging force required to fill the die cavity fully, was as low as 4.1%. To further improve the prediction accuracy of the model for the actual F_f, two rounds of iterative forging experiments were conducted using the Bayesian optimization algorithm, and the prediction error of F_f in the forging experiments was reduced from 6.0% to 1.5%. Finally, the prediction model of F_f combined with a genetic algorithm was used to establish an autonomous optimization strategy for the forging velocity at each stage of the forging stroke, when the billet and die temperatures were disturbed, which realized the autonomous control in response to disturbances. In cases of −20 or −40°C reductions in the die and billet temperatures, forging experiments conducted with the autonomous optimization strategy maintained the measured F_f around the target value of 180 t, with the relative error ranging from −1.3% to +3.1%. This work provides a reference for the study of digital modeling and autonomous optimization control of quality factors in the forging process.