This study investigates the dual impact of Predictive Artificial Intelligence (AI) Analytics implementation on two critical dimensions of modern supply chain management: Operational Efficiency and Environmental Sustainability. Despite the significant theoretical potential for AI to optimize complex logistics and reduce waste, empirical evidence linking the sophistication of AI adoption directly to measurable outcomes in both areas remains fragmented. Using a quantitative, cross-sectional design, data was collected from supply chain managers across 120 manufacturing and retail firms, alongside archival financial and sustainability metrics. The Predictive AI Analytics Implementation Score (measured via survey) served as the primary independent variable. Inventory Turnover Rate (financial data) was used as the proxy for Operational Efficiency, and Material Waste Reduction Percentage (sustainability data) served as the proxy for Environmental Sustainability. Two separate Hierarchical Multiple Regression models were employed to test the hypotheses while controlling for Firm Size and IT Budget. Results indicate a statistically significant positive relationship between AI implementation and both Inventory Turnover Rate (Beta = 0.31, p < .001$) and Material Waste Reduction (Beta = 0.44, p < .001$). These findings suggest that general managers should prioritize targeted AI investments in forecasting and inventory planning to simultaneously achieve financial optimization and environmental goals, contributing to the literature on technology-driven sustainable supply chain excellence (Dolgui & Ivanov, 2022).