Eco-Efficient Smart Manufacturing of Metallic Materials (ECOSMART-MM) aims to bring together researchers and practitioners from academia and industry to explore the integration of advanced computational methods and sustainability principles into modern metallic materials processing. As manufacturing sectors face increasing pressure to reduce energy consumption, waste, and emissions, this Minisymposium will serve as a collaborative platform to discuss transformative innovations that enhance both performance and eco-efficiency. This session will focus on the implementation of Artificial Intelligence (AI), Machine Learning (ML), and digital technologies in traditional and additive manufacturing processes—such as casting, forging, forming, and metal 3D printing[1–4]. Topics include predictive defect modeling, real-time monitoring, digital twins, and reinforcement learning-based process control. Emphasis will also be placed on sustainability-driven innovations like near-net-shape forming, closed-loop recycling, smart energy optimization, and the use of environmentally friendly production practices. In addition to technical innovations, the Minisymposium will address academic-industry collaboration models, including the successful transfer of computational tools and data-driven approaches into industrial production. Speakers will present state-of-the-art research, case studies, and visionary insights to inspire the development of next-generation manufacturing systems that are intelligent, adaptive, and sustainable. This session is intended for researchers, engineers, and decision-makers engaged in computational mechanics, material science, digital manufacturing, and sustainable 2 engineering. The goal is to promote interdisciplinary dialogue and identify actionable strategies to align high-performance metal processing with the imperatives of Industry 4.0 and environmental stewardship.