<p>Reliable underwater object detection (UOD) is critical for applications including marine ecosystem monitoring, aquaculture management, and subsea infrastructure inspection. However, underwater imagery is severely affected by wavelength-dependent attenuation, scattering, turbidity, low illumination, and sensor noise, which collectively degrade visual quality and hinder detection accuracy. Recent advances in deep learning, particularly within the YOLO family of one-stage detectors, have enabled real-time detection with improved robustness under challenging conditions. Despite rapid progress, a clear synthesis of how the latest YOLO variants address underwater-specific degradations remains limited. This study presents a structured review of underwater object detection approaches based on YOLOv8 to YOLOv12, covering both optical imaging and side-scan sonar modalities. A systematic literature review was conducted following PRISMA-based criteria, resulting in the analysis of recent primary studies with emphasis on architectural design, training strategies, datasets, and evaluation protocols. The reviewed methods are organized into a taxonomy distinguishing detector-level innovations from enhancement-assisted pipelines, including physics-guided preprocessing, learning-based restoration, and joint enhancement–detection frameworks. Comparative analysis indicates that improvements in feature fusion, attention mechanisms, lightweight backbone design, and small-object detection strategies significantly enhance performance in degraded underwater conditions. However, persistent challenges remain, particularly in cross-domain generalization, reproducibility, and limited reporting of deployment metrics. Emerging research directions include task-driven enhancement, physics-informed data augmentation, and multi-modal learning approaches. This review provides a consolidated perspective and practical guidance for developing robust and deployable underwater detection systems.</p>