<p>The rapid growth of artificial intelligence has raised increasing concern about its environmental impact, particularly the energy consumed during model training and inference. This study presents MESNET, a lightweight ensemble framework that integrates three convolutional neural network architectures (MobileNetV3-Large, EfficientNetV2-S, and ShuffleNetV2) with real-time carbon emission tracking for sustainable image classification. Experiments were conducted on the CIFAR-10 dataset using a standardized cloud-based environment (NVIDIA Tesla T4 GPU, PyTorch, CodeCarbon). The framework evaluates models not only by accuracy but also by computational cost (GFLOPs), parameter count, energy consumption, and CO₂ emissions. Among individual models, MobileNetV3-Large achieved a strong balance between performance and efficiency, while ShuffleNetV2 demonstrated the lowest FLOPs and emissions. By combining their complementary strengths, the proposed MESNET ensemble achieved 96.98% accuracy, 96.98% F1-score, and produced only 6.863 × 10⁻³&#xa0;kg of CO₂ during training. Energy-normalized metrics further confirmed MESNET as the most efficient model, delivering 6,633 F1-score points per kWh. These results demonstrate that high classification accuracy and environmental sustainability are not mutually exclusive, and that MESNET provides a reproducible and scalable pathway toward Green AI.</p>