The classification of marine animals plays a pivotal role in maintaining ecological balance and supporting sustainable marine practices. However, existing approaches to marine animal classification often face limitations such as inadequate accuracy in complex aquatic environments, a lack of transparency in model decisions, and difficulties in distinguishing between visually similar species. These challenges hinder the broader adoption of AI-powered systems in real-world conservation and monitoring efforts. To address these gaps, a novel approach is proposed that leverages the EfficientNetB7 deep learning architecture combined with Explainable AI techniques such as Grad-CAM visualizations. This approach not only enhances precision but also provides interpretability, offering a transparent solution to understanding model predictions. Through essential preprocessing techniques—data augmentation, normalization, error level analysis (ELA), and feature extraction—integrated into the EfficientNetB7 architecture, the system accurately classifies 23 distinct marine animal species. The proposed model achieves an overall precision, recall, and F1-score of around 80%, with certain species such as otters, urchins, and jellyfish classified with over 90% precision. The incorporation of Grad-CAM visualizations offers insights into the model’s behaviour, highlighting the regions of images that influenced the classification decisions. This transparency ensures that the model’s outputs are trusted and applicable to marine biodiversity monitoring and conservation efforts. Such an approach marks a significant advancement in addressing current limitations in marine animal classification, contributing to the sustainable management of marine ecosystems.