<p>Machine learning (ML) is revolutionizing the development and optimization of composite materials by enabling data-driven approaches for material design, manufacturing processes, and performance prediction. Widely applied in aerospace, automotive, defense, and biomedical engineering, composite materials require precise engineering to achieve targeted mechanical, thermal, and structural properties. Traditional methods, which rely heavily on extensive experimentation and expert knowledge, are often costly, time-consuming, and limited in simulating extreme conditions. ML techniques—including supervised, unsupervised, semi-supervised, and reinforcement learning—allow efficient analysis of complex datasets, pattern recognition, and accurate prediction of material behavior. This review summarizes recent advances in ML applications for composites, covering material design, process optimization, multiscale property prediction, and structural health monitoring. Case studies demonstrate significant improvements in property estimation, defect reduction, and manufacturing efficiency. Challenges such as high-dimensional data, overfitting, and model interpretability are discussed. Addressing these issues is critical for enabling AI-driven development of next-generation composites with enhanced performance, reliability, and sustainability.</p>