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In the field of civil engineering, proper construction waste management is critical to sustainable growth. This study presents a unique decision-making system that uses the Analytical Hierarchy Process (AHP) and Python scripting to improve construction waste management techniques across project scopes. Through an extensive literature assessment, the study analyzes the substantial environmental, economic, and regulatory ramifications of construction waste, emphasizing the need for sophisticated approaches in addressing waste reduction. Using Python as a computational tool, the study uses AHP to evaluate and rank waste management solutions based on environmental impact, cost-effectiveness, and practicality. The findings emphasize the need of incorporating technical tools for developing efficient waste management procedures that not only improve project efficiency but also contribute to larger sustainability goals. This concept provides a scalable solution that can be tailored to various construction contexts, representing a significant advancement in the field of construction waste management. The efficacy of the model is validated by empirical data gathered from several building sites, whereby notable enhancements in waste reduction and resource efficiency are seen. Additionally, the paper explores the policy implications of the results, indicating that incorporating such technology-driven techniques might change CWM practices by bringing them closer to the goals of global sustainability. The research offers a scalable strategy that may greatly improve the sustainability profiles of building projects by integrating cutting-edge computational techniques with real-world construction demands. An effective tool for decision-makers that opens the door to more knowledgeable, effective, and ecologically friendly building methods is the combination of AHP with Python.