增材制造技术在航空航天结构轻量化设计与复杂构件一体化成形等方面具有重要应用前景，但其金属构件因工艺参数、微观组织、缺陷特征及残余应力等多因素耦合作用，疲劳寿命呈现显著离散型与不确定性，寿命预测面临挑战。机器学习为表征工艺—组织—缺陷—寿命之间的非线性关系提供了新途径，但疲劳试验成本高、标签获取周期长和跨平台数据差异等因素使该领域长期面临小样本、高噪声和泛化能力不足等问题。本文围绕小样本条件下增材制造金属疲劳寿命预测问题，系统综述了物理引导机器学习、机理数据增强、多保真建模、机理—数据混合框架、迁移学习与元学习、不确定性量化及概率寿命预测等研究进展，并讨论数据库元数据缺失、评价基准不统一和实验设计与算法评测脱节等问题。最后，结合航空结构高可靠性服役需求，展望了标准化数据库建设、物理一致性数据融合和面向工程认证的统一评估框架等发展方向。

Additive manufacturing (AM) shows great promise for lightweight aerospace structures and the integrated forming of complex components; however, the fatigue life of AM metallic parts exhibits significant scatter and uncertainty due to the coupled influences of processing parameters, microstructures, defects, and residual stresses, making accurate prediction highly challenging. While machine learning (ML) provides a powerful tool for capturing the nonlinear processing–microstructure–defect–life relationships, the high cost of fatigue experiments, long labeling cycles, and cross-platform data heterogeneity lead to persistent small-sample, high-noise, and poor-generalization issues. This review systematically summarizes recent progress in small-sample ML-based fatigue life prediction for AM metals, covering physics-guided ML, mechanism-driven data augmentation, multi-fidelity modeling, hybrid physics–data frameworks, transfer learning and meta-learning, uncertainty quantification, and probabilistic life prediction. We also discuss critical problems including missing metadata in databases, a lack of unified benchmarks, and the gap between experimental design and algorithm evaluation. Looking forward, considering the stringent reliability demands of aerospace applications, we identify key directions such as establishing standardized databases, achieving physically consistent data fusion, and developing unified evaluation frameworks geared toward engineering qualification.