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Green AI in Industrial IoT (IIoT) focuses on sustainable computing with respect to the implementation of energy-saving edge architectures deployed as part of smart manufacturing ecosystems. This study explores the potential of edge computing to achieve energy efficiency through low-power hardware accelerators, adaptive workload scheduling, and AI-assisted energy management to achieve a significant mitigation of carbon footprints without sacrificing high throughput and operations with latency constraints. Pilot deployments in both automotive and electronics production show empirically that edge-based inference minimizes cloud reliance by 40 percent, decreasing the transmission energy charges and enhancing the predictive maintenance and quality control responsiveness to real-time demands. In addition, dynamic voltage and frequency scaling (DVFS) and lightweight deep learning models are able to obtain an energy saving of up to 30% without loss of accuracy in anomaly detection. Case studies point out federated learning at the edge not only improves privacy of data but also reduces network power usage through reduction of unnecessary data transmission. The given architecture takes advantage of micro data centers powered by renewables and smart cooling systems and is consistent with sustainability goals worldwide, including EU Green Deal and UN SDGs. A comparative analysis of it with the traditional cloud-centric IIoT constructions shows that the overall energy consumption decreases by 25-35% which confirmed the possibility of green AI-driven edge solutions. This study therefore offers a tangible way forward whereby industries can incorporate scalable, environmentally friendly IIoT systems that combine efficiency in operations, sustainability and competitiveness in intelligent manufacturing.