Artificial Intelligence-Driven Optimization Techniques in Modern Engineering Systems

Abstract

The integration of artificial intelligence (AI) in engineering has revolutionized the way complex systems are designed, analyzed, and optimized. AI-driven optimization techniques, including machine learning algorithms, genetic algorithms, and deep reinforcement learning, provide engineers with powerful tools to solve multi-variable, non-linear, and large-scale problems that were previously challenging using conventional methods. This study investigates the role of AI in enhancing engineering efficiency across diverse fields such as structural engineering, energy systems, manufacturing, and transportation. Emphasis is placed on the capability of AI to reduce computational costs, improve design accuracy, and enable predictive maintenance. The research also explores emerging hybrid models that combine AI with traditional optimization approaches, offering enhanced adaptability and reliability. Findings suggest that AI not only accelerates innovation but also fosters sustainability and resilience in engineering practices. The paper concludes with challenges related to data quality, interpretability, and ethical considerations, providing insights for future directions in AI-enabled engineering optimization.