Over 300,000 cetaceans die every year due to human activity—primarily ship collisions and fishing entanglements—with roughly 67% of all whale deaths attributed to human interactions. Many whale and dolphin populations are in decline, facing the threat of near‑extinction. The North Atlantic Right Whale, specifically, is critically endangered, numbering only ~350. To combat this issue, a real-time Right Whale detection network is proposed in this paper, to be composed of 3D-printed buoys deployed across the Atlantic Ocean. The goal is to prevent human-caused Right Whale deaths through monitoring whale locations and guiding ships and fishing nets away from collisions through early warnings. The proposed detection network uses a three-step approach where captured audio is recorded on a buoy and then sent to a cloud-based server that processes and classifies whether auditory cues of whales are detected. Finally, client applications are updated using an online API, conveying real-time locations of whales. Each buoy consists of an ESP32, solar panels, and a hydrophone, costing significantly less than current buoys. The system runs a two-branch Ensemble Learning model that employs a 2D Convolutional Neural Network (CNN) with a Custom Convolutional Block Attention Module (CBAM) in one branch, while the other branch utilizes a Bidirectional Long Short-Term Memory (Bi-LSTM) model. Both branches use extracted spectral, temporal, and harmonic features to detect Right Whales based on their vocalizations. This model was trained on datasets from Cornell University and Watkins Marine Mammal Database, achieving a 0.977 AUROC score with a standard deviation of 0.002, surpassing the 0.7214 benchmark set by Cornell while using fewer parameters at 343,877 compared to pre-trained models. In testing, the design demonstrated reliability, potential scalability, and accuracy. Furthermore, promising results were found when extrapolating to the classification of multiple cetacean species, significantly enhancing marine conservation.