Limited data learning is a research area within machine learning focused on developing methods and algorithms capable of building effective predictive or analytical models using substantially smaller datasets than those required by standard data-intensive approaches. This field investigates techniques for achieving high performance and generalization from limited examples, often leveraging prior knowledge, related data sources, or data-efficient model architectures, thereby enabling practical AI applications in data-scarce domains such as rare disease diagnosis, scientific discovery, or specific industrial applications.