Recent interest has focused on integrating Deep Learning (DL) techniques into the Industrial Internet of Things (IIoT) to promote environmental sustainability. This abstract describes how DL methods create a “Green IIoT,” which optimizes resource use, energy waste, and industrial process efficiency. Traditional industrial techniques waste resources and energy, increasing carbon footprints and ecological pressure. IIoT has enabled massive data collection from industrial sensors and devices. However, advanced analytics like DL are needed to use this data for sustainable operations. This abstract discusses how Deep Learning techniques like CNNs, RNNs, and GANs can be used in Green IIoT. DL models can assess sensor data in real-time to predict equipment breakdowns and downtime, decreasing resource waste. DL-powered anomaly detection can also detect industrial process anomalies, reducing energy use and correcting them immediately. DL-based optimization is also essential in Green IIoT. These methods can optimize industrial processes by considering energy costs, production schedules, and environmental impact. Reinforcement learning helps industrial systems cut energy usage and carbon emissions. IIoT systems with DL-powered image and voice recognition automate quality control, waste reduction, and environmental compliance. Green IIoT driven by DL enhances operational efficiency and decision-making by providing actionable insights from complex data patterns. This presentation discusses how Deep Learning and Green IIoT may help the industry. Data analytics and machine learning may help companies save resources and become green. Data privacy, model interpretability, and implementation must be addressed to maximize this amalgamation's potential. Finally, Deep Learning and Green IIoT can change the industry. Advanced DL can assist global businesses in achieving sustainable production, energy efficiency, and environmental impact.