Coral reefs are vital marine ecosystems that support biodiversity and protect coastal regions, but they are increasingly threatened by climate change, pollution, and human activity. Accurate classification of coral reefs is essential for effective monitoring and conservation efforts. Traditional methods of coral reef classification rely heavily on manual surveys and image processing, which are time-consuming and prone to human error. In this research, we propose a novel approach to coral reef classification using deep neural networks (DNNs), leveraging advancements in machine learning and computer vision. The suggested models are trained on a dataset of underwater images to automatically identify and classify different coral species and their health conditions. All the deep learning models perform better in terms of accuracy and speed, providing a scalable solution for large-scale reef monitoring. This study highlights the potential of deep learning in automating marine biodiversity assessment and offers insights into the development of AI-driven tools for environmental conservation.