<![CDATA[Global environmental pressures have pushed the manufacturing sector to adopt sustainable operations. This study investigated the integration of Artificial Intelligence into Green Supply Chain Management strategies in the manufacturing industry in Malaysia. Researchers applied a quantitative explanatory design to collect primary data from managers in medium and large companies. The research team distributed structured questionnaires to evaluate the impact of smart technology on green procurement, green manufacturing, and green logistics. Data analysis used structural equation modeling tools to test the causal relationships between variables. The research findings showed that the implementation of Artificial Intelligence significantly improved the effectiveness of all sustainable supply chain practices. Field data has proven that predictive algorithms and analytical systems optimize the search for environmentally friendly materials, minimize production emissions, and streamline reverse logistics cycles. This series of optimized practices has been shown to substantially improve companies' environmental and operational performance. This empirical investigation has concluded that mastery of advanced analytical technology serves as a crucial technical prerequisite for achieving ecological sustainability targets without sacrificing profitability. This research has provided empirical evidence that data-driven supply chain ecosystems empower the manufacturing sector to meet stringent environmental standards while maintaining increased efficiency and market competitiveness.]]>
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