This survey provides a comprehensive synthesis of methods, datasets, metrics, and deployment strategies from the evolution of convolutional neural network (CNN)-based detectors to emerging transformer and hybrid architectures. It unifies fragmented literature into a structured taxonomy while integrating results from 2014–2025 studies. The paper reviews benchmark datasets, discusses evaluation protocols and reproducibility standards, and proposes a deployment playbook considering latency, energy, and hardware constraints. Beyond technical performance, it addresses responsible AI practices and ethical challenges in marine observation. By highlighting open problems in multimodal fusion, self-supervised learning, and on-device adaptation, this work aims to guide future research and practical deployment of underwater vision systems. A comprehensive survey of underwater object detection covering classic CNN-based detectors, modern transformer and hybrid models, training and evaluation practices under challenging aquatic conditions, the dataset landscape, deployment constraints (latency/VRAM/energy), and open problems for real-world marine applications.