Integration of Artificial Intelligence Techniques in Mechanical Engineering Systems for Design Optimization and Predictive Analysis

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly transformed traditional mechanical engineering practices by enabling intelligent decision-making, automation, and data-driven optimization. This paper presents a comprehensive review of AI applications in mechanical engineering, focusing on design optimization, predictive maintenance, manufacturing processes, and thermal–fluid system analysis. Machine learning algorithms, neural networks, and deep learning models are explored for their ability to analyze complex datasets, reduce computational time, and improve system performance compared to conventional methods. Case studies from areas such as smart manufacturing, fault diagnosis in rotating machinery, and AI-assisted computational fluid dynamics (CFD) demonstrate the effectiveness of AI-based approaches. The study highlights current challenges, including data quality, model interpretability, and integration with existing engineering workflows, while also discussing future research directions. The integration of AI in mechanical engineering is shown to enhance efficiency, reliability, and sustainability, making it a key enabler for next-generation engineering systems.

Cite as:

S. Mulani. (2026). Integration of Artificial Intelligence Techniques in Mechanical Engineering Systems for Design Optimization and Predictive Analysis. Integration of Artificial Intelligence Techniques in Mechanical Engineering Systems for Design Optimization and Predictive Analysis, 9(1), 9–16. https://doi.org/10.5281/zenodo.19125794