Continuous prepreg filament fabrication involves complex interactions between process parameters and forming performance, limiting the effectiveness of conventional optimization methods. This work investigates continuous carbon fiber–reinforced polyamide (CCF/PA) prepreg filaments using a self‑developed platform to evaluate these interactions and address these limitations. Thirty‑group process‑performance data were obtained via customized orthogonal‑like experiments with three process inputs and two performance outputs. A backpropagation (BP) neural network was built, and a genetic algorithm (GA) was employed to optimize its initial weights and thresholds for constructing a GA–BP prediction model. On this basis, nondominated sorting genetic algorithm II (NSGA‑II) was adopted for process multi‑objective optimization to balance production efficiency, mechanical properties, and dimensional quality. The results indicate that the proposed GA–BP model achieved high prediction accuracy, with determination coefficients (R²) of 0.964 and 0.945 for tensile force and roundness, respectively. The multi-objective optimization yielded an impregnation temperature of 264.8 °C, a traction speed of 1.49 m/min, and a sizing temperature of 329.4 °C. Experimental validation shows that the optimized prepreg filament achieved a tensile strength of 1,381 MPa and a roundness of 0.016 mm, consistent with the model predictions. The proposed coupled optimization system provides an innovative and efficient technical route for the high-precision fabrication of thermoplastic composite prepreg filaments. It can effectively reduce repetitive trial-production costs, stabilize the tensile mechanical properties and dimensional roundness of prepreg filaments, and offer a universal data-driven optimization strategy for the large-scale industrial production of thermoplastic prepreg filaments.