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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.