The increasing complexity of industrial systems and the convergence of digital and physical components present unique challenges for coupled problem-solving in engineering. Traditional computational models often struggle to balance precision, speed, and resource efficiency, especially when addressing multiphysics and multiscale phenomena. Scientific Machine Learning (SciML), an emerging fusion of numerical analysis and machine learning, offers promising avenues to tackle these challenges. By integrating domain knowledge with data-driven methods, SciML enables the creation of computationally efficient, interpretable, and robust models for coupled systems. This talk will highlight recent advancements in SciML relevant to coupled problems, focusing on key areas such as physics-informed neural networks, active learning for efficient data utilization, and model validation in industrial contexts. Case studies will illustrate the potential of SciML to optimize workflows, reduce computational costs, and improve accuracy in coupled problem scenarios, ranging from material behavior modeling to complex fluid-structure interactions. We will also address challenges in scalability, data management, and industrial adoption, proposing strategies to bridge the gap between academic innovations and industrial applications. By fostering interdisciplinary collaboration and advancing SciML methodologies, we can redefine paradigms in engineering and unlock new opportunities for addressing coupled problems across industries.