Artificial Intelligence Applications in Engineering Systems: A Review

The rapid advancement of Artificial Intelligence (AI) has profoundly transformed the design, operation, and optimization of contemporary engineering systems. This review critically examines the scope, methodologies, and impact of AI applications across major engineering domains, including mechanical, electrical, civil, industrial, and computer engineering. Drawing on recent scholarly literature, the study synthesizes how machine learning, deep learning, neural networks, evolutionary algorithms, and intelligent control systems are integrated into engineering processes for prediction, optimization, automation, fault detection, and decision support. Particular attention is given to applications such as smart manufacturing, predictive maintenance, structural health monitoring, power and energy systems, robotics, transportation, and intelligent infrastructure. The review also discusses enabling technologies such as big data, the Internet of Things (IoT), and cyber–physical systems that facilitate AI-driven engineering solutions. In addition to highlighting performance improvements and efficiency gains, the study critically addresses key challenges, including data quality, model interpretability, computational complexity, ethical concerns, and system reliability. By identifying prevailing trends, research gaps, and future directions, this review provides a comprehensive reference for researchers and practitioners seeking to understand and advance the role of AI in engineering systems.

The review adopts a systematic literature synthesis approach, drawing on recent scholarly works to examine AI applications across engineering domains. It focuses on core AI methodologies such as machine learning, deep learning, neural networks, evolutionary algorithms, and intelligent control systems. The synthesis covers their integration into engineering processes for prediction, optimization, automation, fault detection, and decision support. Enabling technologies like big data, IoT, and cyber–physical systems are also analyzed as facilitators of AI-driven solutions. The methodology includes a critical assessment of performance improvements, efficiency gains, and challenges such as data quality, model interpretability, computational complexity, ethical concerns, and system reliability.