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This research investigates the transformative role of Artificial Intelligence (AI) in demand forecasting as a critical driver for organizational waste reduction and environmental sustainability. By synthesizing contemporary literature and empirical case studies, the study demonstrates that AI-driven predictive models outperform traditional reactive methods by integrating real-time data and diverse external variables. The findings establish a clear causal pathway: increased forecasting accuracy directly mitigates overproduction and stockouts, significantly reducing physical waste and CO₂e emissions. However, the study also identifies critical barriers to adoption, including data silos, poor data quality, and organizational resistance. The research concludes that while AI implementation requires substantial strategic investment in data governance and cultural change, it is a vital catalyst for transitioning toward resilient, proactive supply chains. Ultimately, the successful alignment of AI with lean management principles enables organizations to achieve triple-bottom-line objectives, fostering long-term productivity and a more sustainable global economy.