Coral reefs are vital marine ecosystems that play a crucial role in supporting biodiversity, providing coastal protection, and driving global fisheries and tourism economies. However, these ecosystems are under increasing threat from coral bleaching, driven by climate change and human-induced stressors. Monitoring and mitigating coral bleaching require scalable, automated systems capable of accurate detection and classification. This study presents a synthesis of state-of-the-art deep learning models, including VGG16 and YOLO, for coral health assessment. Additionally, a novel annotated coral bleaching database is introduced, designed from diverse video and image sources to train machine learning models effectively. Our results demonstrate that these approaches not only improve the accuracy of coral health classification but also enhance the scalability and reliability of reef monitoring efforts, supporting marine conservation initiatives. Future work focuses on expanding datasets and exploring advanced architectures such as ResNet50 and EfficientNet to improve generalization and robustness.