Traditional approaches to Engineering Design (ED) processes are based on intuition involving scientific, experimental, creative methods, and expert knowledge of the process. For instance, traditional process planning is executed primarily based on experience in creating products using machine tools. ^{1, 2}

Recent advancements and the rapid adoption of artificial intelligence approaches have simplified and revolutionized engineering design. ³ Artificial Intelligence (AI) is known as “using computer algorithms to imitate biotic mental processes or activities” ⁴ to execute tasks such as learning about processes, understanding, estimating, problem-solving and decision-making in several areas of engineering applications that cut across all disciplines.

The adoption of this novel methodology transforms how processes are designed and executed in the shortest possible time, with the least cost, precise solutions and further completion rates than generic human approaches. ⁵

The desire for better solutions to challenging world problems has led engineers to push for optimal solutions using modern technologies to conceptualize, evaluate, and compare many candidate solutions without the need for physical prototypes, ⁶ using computational tools and Computer-Aided Design (CAD) systems. ⁷ These systems have evolved significantly with their sophisticated modelling, simulation and analysis capabilities. ⁸ AI has further transformed these systems by introducing automation, decision-making support and advanced analytic support through Machine Learning (ML), Neural Networks (NN), and Generic Algorithms (GA), which are now integrated into CAD systems, making it possible to explore and execute complex and data-driven tasks. ^{9, 10}

This study provides a comprehensive review of the development and applications of AI in Engineering Design over three broad clusters of application: AI technologies in the ED, Engineering Design Methodologies and their practical applications across engineering.

To highlight the impact of AI, this study reviewed several studies using AI techniques approximately 1653 studies were found between 1970–2025, a filter was used to narrow the review to between 2010–2025 which reduced the number to 142, on subject areas limited to engineering, energy and chemical engineering showed 763 results from articles, conference papers, reviews, conference reviews etc, and the keyword search showed 5962 with a minimum of five occurrences resulting in over 316 keywords:- engineering design, artificial intelligence, deep learning, machine learning, product design, design, optimization, decision support systems etc., as shown in Figure 1.

To showcase the application domains of AI techniques to ED, the following section provides several situations backed by the research conducted:

Mechanical Engineering: One of the main areas that has revolutionised traditional design is Computer-Aided Design (CAD) systems. Since its introduction in the sixties (1960) the use of computer technology has helped designers to enhance their work. ²⁵ From traditional two-dimensional drawing tools to complex three-dimensional modelling, simulation and optimization systems. ²⁶ CAD technology has become essential in various fields such as engineering, mechanical design, automotive, and industrial design. ²⁷

AI has transformed CAD systems with tools for automation, decision-making support and advanced analytical capabilities through machine learning, neural networks and generic algorithms, which are integrated into CAD software to handle complex and data-driven tasks. ²⁸

AI technology has improved CAD systems and predicted possible problems during design and proffer solutions, which subsequently reduces the uncertainties in the design processes, and increases reliability and efficiency. ^{27, 29} AI algorithms have also boosted the capabilities of CAD systems to generate generative design, offering multiple design solutions that suits different situations. Through this approach, numerous design possibilities, optimizations of functionalities and visual appeals have become achievable. ^{7, 30}

Another challenging task is the design of mechanical mechanisms subjected to complex physical constraints. ²⁶ Understanding the relationship between motion and mechanism design is essential because of the non-linear characteristics between the mechanism classes and boundary conditions. ²⁶ When solved analytically, an in-depth knowledge of the mechanism is required. AI has helped designers to optimize these designs through simulations and data-driven methods to train data that will provide new designs with little calculation. ³¹ Several AI techniques, such as conditional adversarial networks, natural-language models and autoencoder networks are used for the synthesis of mechanisms, creation of multibody simulation codes or models for rapid prototyping, and representation of target paths in a compressed lower-dimensional feature space. ^{28, 31– 37}

The evolution of AI and its applications is finding ground in various fields, and is not limited to engineering design. In engineering design, AI is utilized for several purposes, such as CAD systems to automate tasks based on predefined rules, integrating advanced techniques that integrate graphical, verbal and gestural inputs into refined design concepts, predictive analytics and real-time monitoring to enhance systems.

Data transformation is also an area that is critical in engineering design, because of its scarcity, high cost of acquisition and limited access during operations. AI techniques help in developing minimal datasets that can address such issues showcasing significant variations in system responses with small parameter changes making simulations practical, and to setting benchmarks to predict system performance and requirements of data-driven models.

The integration of AI techniques also enhances design optimization, as it offers sophisticated tools to aid in rapid design concepts and reduced lead times to produce new products in the market. These techniques have demonstrated their versatility in all aspects of business, manufacturing, design and life, particularly in engineering design where AI is transforming computer software to make data-driven decisions, and simulate intelligent behaviors to enhance efficiency and product quality.

In conclusion, the rapid evolution of digital ecosystem AI in engineering design has the potential to drive the engineering industry and provide systematic and dynamic capabilities to transform traditional engineering design processes into enhanced capabilities.