Predictive Framework for Sustainable Engineering through Machine Learning and Cross-Sector Collaboration

Accomplishing Sustainable engineering rests on careful collaboration across disciplines to solve challenges of the modern world; this includes climate change, resource management, and socio-economic issues. Still, collaboration across the different sectors of AIG (academia, industry, and government) is poorly integrated due to operational silos and structures lacking a centralized data-driven approach. This research proposes a new methodology based on ensemble machine learning, assessing, and predicting the outcomes of engineering projects with real-time open-source data focused on sustainability. Unlike traditional rule-based and qualitative evaluations, the proposed method measures diverse parameters like funding streams, policy advocacy, and stakeholder participation to construct a comprehensive model of collaboration at cross-sectoral levels. Harvested public data was normalized, encoded, and pre-processed class balances using SMOTE. An Extra Trees Classifier was trained to perform binary classification of project success deemed as primary indicators, evaluating performance through accuracy, precision, recall, F1 score, log loss, and MSE. The model achieved 96% accuracy and 0.733 F1 score. Feature importance analysis corroborated interpretability of model predictions. These results underscored the drivers of effective collaboration while showcasing the model's robust predictive capacity. Beyond bridging the literature gap, this study equips policymakers and other stakeholders with actionable insights to enhance strategic planning, resource distribution, and governance in sustainable engineering.