Abstract: Metals with a high strength-to-weight ratio are being increasingly used in the automobile industry to achieve a reasonable tradeoff between weight reduction, crashworthiness, fuel efficiency, and environmental friendliness. However, sheets of lightweight metals such as advanced high strength steel, aluminum alloy, magnesium alloy, and titanium alloy, tend to crack without obvious necking during widely-used stamping processes. In particular, so-called shear-induced ductile fracture, which occurs near the pure shear loading path, exceeds the prediction spectrum of traditional necking-based forming limit curves. In addition, the single point incremental forming (SPIF) process, which is currently under rapid development because of its high flexibility in rapid prototyping or customized production process, demonstrates a strong necking suppression. Consequently, ductile fracture without distinct necking has been considered as the forming limit for SPIF. Although the classical forming limit prediction approach, which is, in principle, based on necking instability, has been widely applied as a standard solution for predicting failures in the process of sheet-metal forming, it barely provides feasible solutions to the aforementioned issues. This limitation greatly restricts the application of lightweight materials and the development of novel forming processes. Therefore, researchers have devoted increasing attention to accurately predicting the ductile fracture of metallic materials. In the current paper, we first review studies related to the micro-mechanisms that trigger ductile fracture. We then systematically review ductile fracture prediction models in two categories:coupled models and uncoupled models. Model applications in metal forming processes are summarized as well. Toward the conclusion, prospective trends in ductile fracture research are surveyed. The objective of this paper is to provide engineers and researchers with a beneficial overview of the selection, utilization, and development of ductile fracture prediction models.