<p>Dynamic Label Assignment (DLA) has emerged as a critical mechanism in mod- ern deep learning based object detection, governing how model predictions are matched to ground-truth instances during training. Conventional IoU-threshold based assignment strategies often exhibit brittle behavior in practical scenar- ios involving small objects, dense layouts, heterogeneous object shapes, noisy annotations, and domain shifts. In contrast, DLA methods adaptively determine positive and negative samples using statistical criteria, probabilistic modeling, global optimization, or learnable scoring functions, leading to improved training stability and detection accuracy. This survey presents a comprehensive and unified analysis of over seventeen representative DLA approaches, including ATSS, PAA, OTA/SimOTA, TOOD, Hungarian matching in DETR, and recent learnable and meta-assignment strate- gies. We propose a novel taxonomy that categorizes existing methods into four principal families: statistical, probabilistic, optimization-based, and learnable assignment. Building on this taxonomy, we introduce a unified computational framework that formulates diverse DLA strategies as instances of a generalized weighted cost minimization problem, revealing shared structural principles across seemingly disparate designs. We further analyze these families in terms of computational complexity, con- vergence characteristics, sensitivity to small objects, robustness to annotation noise, and generalization across domains. Through application-driven discus- sions spanning autonomous driving, robotics, remote sensing, medical imaging, and industrial inspection, we demonstrate the central role of DLA in real-world perception systems. Finally, we outline open challenges and future research directions, including scalable optimal transport, uncertainty-aware assignment, interpretability, transformer-native matching, and multi-modal integration.</p>