Functionally Graded Materials (FGMs) are advanced composites characterized by a continuously varying microstructure, able to mitigate abrupt interfaces, providing a smooth gradient of properties and mitigating stress concentrations typical of traditional laminates [1]. Initially designed to reduce the thermal stresses arising from the high temperatures of the metal and ceramic interfaces in a space shuttle project [2], their ability to tailor materials mixture’s distributions according to specific operating requirements, without the disadvantages shown by traditional laminates, was rapidly noticed in different Science and Engineering fields [3]. The use of optimization techniques and more recently of machine learning algorithms is contributing in a significant manner to obtain enhanced and more representative models of the complex relationships between FGMs’ composition, their properties and performance at different scale levels. This mini-symposium aims to highlight the recent advances in the broad scope of the modelling, analysis and optimization of functionally graded composites and structures.