The study of marine mammals is essential for understanding and protecting ocean ecosystems. Acoustic monitoring provides a non-invasive and efficient way to track their activity, but traditional methods often fail in noisy and variable underwater conditions. This work presents a deep learning framework for acoustic recognition of marine mammals using the VGGish model, a convolutional neural network pre-trained on largescale audio datasets. The model was fine-tuned to detect species specific acoustic signals after applying targeted preprocessing techniques to enhance feature quality. Rigorous training and validation on curated datasets produced a test accuracy of 95.61%, representing a clear improvement over conventional approaches. The framework is reliable, scalable, and suitable for integration into real-time detection systems to support conservation planning in regions with high acoustic activity. Future work will focus on expanding the dataset and enabling full real-time deployment to further strengthen its value for marine mammal research and biodiversity monitoring.