This study explores the use of Convolutional Neural Networks (CNNs) for the automated identification of marine species, aiming to enhance biodiversity monitoring and conservation efforts. We developed a robust CNN model trained on a diverse dataset of annotated marine creature images. Utilizing techniques such as data augmentation and transfer learning, our model achieved over 90% accuracy in classifying marine species. The system's performance was tested under various underwater conditions, demonstrating its robustness and reliability. This automated approach promises to significantly reduce the manual labor involved in species identification, offering an efficient tool for marine biologists and conservationists. Future work will expand the dataset, refine the model, and integrate it into real-time identification platforms. The underwater world teems with a diverse array of marine life, underscoring the need for precise identification in ecological research and conservation endeavors. This study is dedicated to creating a reliable and efficient classification system for sea creatures, employing CNN architectures. Leveraging the capabilities of deep learning, our proposed system aims to accurately identify various sea creatures in real-time underwater images. In contrast to existing methods reliant on Electrocardiogram (ECG) signals, our approach encompasses a broader dataset featuring fish, corals, marine mammals, and invertebrates. Each image is carefully labeled with the corresponding species, facilitating supervised learning. Experimental findings showcase the CNN architecture’s effectiveness and accuracy in sea creature classification. With its adaptability to diverse underwater conditions and real-time capabilities, this system emerges as a crucial asset for marine research and conservation initiatives.