The Marchenko method reconstructs full-wavefield Green’s functions to produce images free from internal multiple artifacts, but the focusing function estimation process is computationally expensive. We propose an optimization-based solver that uses a pre-trained network as a fixed forward operator, enabling efficient estimation via automatic differentiation. This reduces computation while maintaining good image quality, as demonstrated on synthetic and Volve field data, offering a practical solution for large-scale seismic imaging.