<![CDATA[The construction industry significantly contributes to global energy consumption and carbon emissions, necessitating innovative solutions for sustainable supply chain management. This study presents an AI-Enabled Smart Web-Based System for Monitoring Sustainable Energy Usage in Construction Supply Management, which innovatively integrates AI-driven energy prediction directly into procurement workflows. The system, developed using the Waterfall model and built upon a Laravel-MySQL-Python (Scikit-Learn) architecture, provides a robust platform for real-time procurement tracking and AI-driven energy usage prediction. Functional testing verified the system's operational reliability, achieving a 100% pass rate. The integrated AI prediction module demonstrated high accuracy with a Mean Absolute Error (MAE) of 0.62 kWh, effectively forecasting energy consumption associated with procurement transactions. User evaluations indicated a 92% overall satisfaction rate, highlighting significant improvements in data transparency, report generation speed, and enhanced understanding of energy-related impacts. This system offers a novel approach to bridge the critical gap between operational efficiency and environmental sustainability in construction supply chains, providing a scalable model for intelligent, energy-aware process transformation. This research contributes a scalable model for fostering sustainable digital transformation within the construction sector, aligning with global sustainability goals and promoting eco-efficient practices.]]>
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