Illegal and unsustainable wildlife trade remains a major conservation challenge and is increasingly shifting to online marketplaces where monitoring is difficult. To address this gap, we used real-world images from online platforms, enforcement seizures, and market inspections to develop a deep-learning object detection framework to identify products derived from three highly traded species: elephant, pangolin, and tiger. Our dataset of 39,941 images included both wildlife products and visually similar non-wildlife items, reflecting real enforcement complexity. We used cross-entropy and asymmetric focal loss functions to train Faster Region-based convolutional neural network (Faster R-CNN) models and compared species-specific and multispecies training strategies. The best-performing model, trained with cross-entropy on all species combined, achieved an overall accuracy of 84.2%, with high performance for pangolin (93.6%) and tiger (90.3%) and moderate performance for elephant (71.1%). Although asymmetric focal loss increased recall, it substantially reduced precision. We deployed the optimized model in a prototype smartphone application, potentially usable in real-world settings for monitoring online images. With an independent test set, the application achieved 91.3% accuracy and an F-score of 0.86. These results demonstrate the potential of object detection models to support scalable, real-time monitoring of wildlife trade and enable the rapid triage of suspicious posts, regulatory compliance, and conservation decision-making.

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