It has been observed recently that a new shift has taken place in composite design for industrial and automobile applications due to inclusion of artificial intelligence(AI) into material design for engineering applications. Traditional composite design methods usually depend on data driven modelling and experimentation which are very costly, time wasting, and always have limited optimization capacity. This review analysed how AI approaches, such as deep learning(DL), machine learning(ML), data driven optimization can expedite material design, forecasting and performance enhancement.

With the integration of AI, it could be possible to evaluate complex dependencies between processing factors, material constituents, and performance features that include thermal behaviour, mechanical properties, stiffness, and weight efficiency. Experimental efforts through optimization of filler selection, reinforcement dispersion with the use of big data sets and algorithm can be minimised with the help of AI models. This makes it very easier develop lightweight composite materials that has enhanced strength to weight ratios for automobile applications which raises fuel economy and lessen emissions. AI-driven design in manufacturing environments promote growth of advanced materials with custom-built elements for certain conditions.

The failure analysis, longevity prediction, real time monitoring of composite parts can be evaluated with AI and this improves maintenance plans and reliability. Accuracy and adaptivity in composite development can be further improved by integrating AI with advanced technology methods such as intelligent manufacturing systems and additive manufacturing.

This study clearly reviews how applying AI significantly reduces development times, reduces material waste, and increases the effectiveness of composite material design. The findings demonstrate that AI-driven techniques offer a powerful path toward next-generation, high-performance composite materials, enabling businesses to meet the growing needs for productivity, sustainability, and innovation.