Additive manufacturing (AM) has brought about a revolution in the way we can manufacture complex products with customized features. AM has paved its way in the application areas ranging from aerospace, automotive, consumer to biomedical. AM of composites has attracted special attention due to its promise in improving, modifying, and diversifying the properties of generic materials through introducing reinforcements. This review provides a detailed landscape of fiber‐reinforced composites processed via AM techniques. Different AM processes, various material formulations, and strengths and drawbacks of AM methods are discussed. Emphasis is paid to AM techniques focusing on continuous fibers, as they hold the promise of becoming the next‐generation composite fabrication methodology. The article also tries to identify the potential of AM technology for fiber‐reinforced composites and delves into challenges facing the area.

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well‐curated datasets, thereby unveiling latent knowledge crucial for informed decision‐making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM‐printed parts. This review delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML‐related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation), and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM‐related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM‐related applications. It is envisioned that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM‐related research areas.