  Additive Manufacturing (AM) is a transformative manufacturing technology
enabling direct fabrication of complex parts layer-be-layer from 3D modeling
data. Among AM applications, the fabrication of Functionally Graded Materials
(FGMs) has significant importance due to the potential to enhance component
performance across several industries. FGMs are manufactured with a gradient
composition transition between dissimilar materials, enabling the design of new
materials with location-dependent mechanical and physical properties. This
study presents a comprehensive review of published literature pertaining to the
implementation of Machine Learning (ML) techniques in AM, with an emphasis on
ML-based methods for optimizing FGMs fabrication processes. Through an
extensive survey of the literature, this review article explores the role of ML
in addressing the inherent challenges in FGMs fabrication and encompasses
parameter optimization, defect detection, and real-time monitoring. The article
also provides a discussion of future research directions and challenges in
employing ML-based methods in AM fabrication of FGMs.
