Accuratesnake species identification is essential for effective snakebite management, particularly in regions like Morocco, where approximately 400 snakebite incidents are reported annually, with a case fatality rate of 7.2%. Identifying venomous snakes promptly can significantly improve treatment outcomes by enabling the timely administration of specific antivenoms. However, the absence of comprehensive databases and rapid identification tools for Moroccan snake species poses challenges to effective clinical responses. This study presents a deep learning-based approach for the automated identification of Moroccan snake species. Several architectures, including VGG-19, VGG-16, and EfficientNet B0, were evaluated for their classification performance. EfficientNet B0 emerged as the most effective model, achieving an accuracy of 92.23% and an F1-score of 93.67%. After training on the SnakeCLEF 2021 dataset and fine-tuning with a specialized local dataset, the model attained a validation accuracy of 94% and an F1-score of 95.86%. To ensure practical applicability, the final model was deployed on a web platform, enabling the rapid and accurate identification of snake species via image uploads. This platform serves as a valuable tool for healthcare professionals and the general public, facilitating improved clinical response and educational awareness. This study highlights the potential of AI-driven solutions to address challenges in snakebite identification and management, offering a scalable approach for regions with limited resources and high snakebite prevalence.