The integration of machine learning (ML) with the inverse scattering method opens new avenues in addressing the complexities of the Nonlinear Schrödinger Equation (NLSE).The presentation begins with an overview of the NLSE's significance in modeling light propagation in optical fibers, emphasizing the challenges introduced by nonlinearities on signal integrity. It progresses to explore the inverse scattering method as a robust analytical solution to the NLSE, crucial for enhancing the understanding and mitigation of nonlinear effects in fiber optics. Moving toward interdisciplinary innovation, the focus shifts to how machine learning techniques provide new approaches to solving the inverse scattering problem, thereby promising significant advancements in the design and efficiency of optical communication networks. Bridging traditional analytical methods with the computational power of machine learning, the session aims to foster  innovative strategies for overcoming complex challenges in coherent communications and beyond.