The underwater environment is teeming with diverse marine life, playing a crucial role in the Earth's ecosystem. However, identifying and classifying marine organisms in their natural habitat is a challenging task due to the complex and dynamic nature of underwater scenes. In recent years, advancements in computer vision and deep learning have opened up new possibilities for automated species classification. Conventional methods for underwater species classification typically rely on manual identification by marine biologists or taxonomists. While these experts possess invaluable knowledge, the process is labour-intensive, time-consuming, and may be limited by human subjectivity and availability. The primary challenge in this context is to develop a system capable of accurately classifying marine organisms based on images or videos captured underwater. This involves handling the unique challenges of underwater imaging, including variations in lighting, water clarity, and the complex backgrounds of aquatic environments. Therefore, the need of Accurate and efficient classification of marine species is essential for ecological research, conservation efforts, and sustainable marine resource management. Traditional methods of species identification often rely on manual observation, which can be time-consuming and subject to human error. Leveraging technology to automate the process holds the potential to greatly accelerate and improve the accuracy of species classification in underwater environments. The project, "Utilizing Convolutional Neural Networks for Underwater Species Classification: A Case Study on Marine Organisms," aims to revolutionize underwater species identification by employing advanced computer vision techniques, particularly CNNs. By training models on extensive datasets of underwater imagery, this research endeavors to develop a system capable of autonomously and accurately classifying marine organisms. CNNs are adept at learning hierarchical features from images, making them well-suited for the complex visual patterns encountered underwater. This advancement holds the potential to significantly advance marine biology research, conservation efforts, and resource management by providing a powerful tool for rapid and precise species classification in underwater environments.