This submission contains two technical resources produced for the U.S. Department of Energy Water Power Technologies Office (WPTO) that evaluate and advance automated methods for underwater wildlife detection, tracking, and monitoring around marine energy systems. The project analyzes how modern computer-vision and machine-learning techniques can support reductions in manual labor, improve monitoring consistency, and streamline permitting by enabling automated detection of fish in both optical and imaging-sonar data. The work includes development and testing of You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (R-CNN), Elastic Shape Analysis (ESA), and hyper-image methods for fish detection using the Pacific Northwest National Laboratory (PNNL) EyeSea optical dataset and multiple imaging sonar datasets. It also provides a comprehensive literature review on imaging sonar performance, machine-learning approaches for object detection, and domain-adaptation strategies to support cross-site generalization.