Abstract

The convergence of advanced engineering systems with computational intelligence is reshaping the technological landscape, driving unprecedented capabilities in system design, analysis, and operational optimization. This article provides a comprehensive review of the integration of intelligent modeling frameworks, optimization methodologies, and sustainable technological innovations within contemporary engineering practice. Against the backdrop of Industry 4.0 and the pressing demands of environmental sustainability, the article examines how computational approaches—including machine learning, digital twins, and Multiphysics simulation—are enabling more sophisticated engineering solutions. Key frameworks such as data-driven modeling, hybrid-augmented intelligence, and neural operators are analyzed for their capacity to enhance predictive accuracy and computational efficiency. The role of optimization techniques, from classical methods to AI-based metaheuristics, is evaluated in terms of their contribution to sustainable engineering outcomes across sectors including manufacturing, energy systems, and infrastructure development. Particular attention is given to real-world applications where computational engineering frameworks facilitate sustainable innovations, such as smart grids, eco-efficient manufacturing, and intelligent transportation systems. The article concludes by identifying persistent challenges related to scalability, data integrity, and implementation barriers, while outlining future directions for research at the intersection of computational engineering and sustainability. This synthesis underscores the transformative potential of integrated computational approaches in advancing both engineering capability and environmental responsibility.