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Object detection technologies form the foundation of real-time performance across a broad spectrum of applications, ranging from autonomous systems to medical imaging. This study analyzes the extensive architectural evolution of the YOLO series, the benchmark for this field, from its initial version to the cur rent YOLO26 model. Throughout the paper, structural transformations in the backbone, neck, and detection head components are examined chronologically. The review focuses on critical technical milestones, including the transition from anchor-based to anchor-free systems, the integration of attention mechanisms, and optimizations to loss functions. Furthermore, by evaluating the density of literature, data labeling tools, and the wide range of applications, the study exam ines the evolutionary logic of object detection architectures and the technological framework of modern models in comprehensive detail.