Asian Journal of Advanced Research and Reports

This work aims to develop an AI-driven tool that can enhance the project management construction to be environmentally friendly and also efficient. The provided tool predicts the schedule delays and improves resource allocation through combining the project data, Random Forest algorithms, and important environmental parameters which include waste production and carbon emissions. A graphical interface based on Python that is easy to use has been developed to support in the scenario analysis and decision-making. We used cross-validation on a real-world building dataset to investigate how well the model worked. It achieved an average R² of 0.87 and a 15% lower mean absolute error than baseline approaches. Sensitivity analysis further demonstrated that the tool has the ability to balance between operational efficiency and environmental objectives. The results show that incorporating sustainability factors directly in the prediction model can greatly lower the project overruns and environmental issues. This work addresses a major gap in the digital construction management by providing the professionals and researchers with a practical, evidence-based procedure to ensure that projects satisfy environmental objectives. The study underscores the need for intelligent systems in construction management to reduce project inefficiencies and promote a more sustainable built environment.