The rapid individualization of milling operations have introduced unprecedented complexity and variant diversity, necessitating adaptive process monitoring under scarce-data conditions. This systematic literature review (SLR) takes a comprehensive look at machine learning (ML)-based approaches for process monitoring and anomaly detection in highly flexible milling processes, focusing on single-part and rapidly changeing production scenarios. The fourteen most relevant studies published since 2019 were identified by adhering to established SLR frameworks. The methods are evaluated in terms of their flexibility, data efficiency, model quality and cost-effectiveness. It is revealed by the SLR that transfer learning (TL), physics-informed ML and active learning (AL) are frequently used to address the issue of limited training data whilst improving the robustness of the model. However, there a shortcoming in the integration of multiple data-efficient training strategies within holistic frameworks. Additionally, focusing on internal machine signals could reduce the burden of monitoring systems on brownfield machines. Combining monitoring via internal machine signals with AL, TL, physics-informed ML and data augmentation offers promising research directions for scalable, low-cost process monitoring in flexible manufacturing environments.