▎ 摘　　要

Advancement of novel electromagnetic inference (EMI) materials is essential in various industries. The purpose of this study is to present a state-of-the-art review on the methods used in the formation of graphene-, metal- and polymer-based composite EMI materials. The study indicates that in graphene- and metal-based composites, the utilization of alternating deposition method provides the highest shielding effectiveness. However, in polymer-based composite, the utilization of chemical vapor deposition method showed the highest shielding effectiveness. Furthermore, this review reveals that there is a gap in the literature in terms of the application of artificial intelligence and machine learning methods. The results further reveal that within the past half-decade machine learning methods, including artificial neural networks, have brought significant improvement for modelling EMI materials. We identified a research trend in the direction of using advanced forms of machine learning for comparative analysis, research and development employing hybrid and ensemble machine learning methods to deliver higher performance.