E-waste management (EWM) refers to the operation-management of discarded or unproductive electronic devices and components, a challenge exacerbated due to overindulgent urbanization. This article presents a multidimensional cost-function-based analysis of the EWM framework structured on three modules - environmental, economic, and social uncertainties - which contemplate the 3 pillars of sustainability in an e-waste recycling plant, including the production-delivery-utilization process. The framework incorporates material recovery from a single e-waste facility provisioning for chemical and mechanical recycling. Each module is ranked using independent Machine Learning (ML) protocols: a) Analytical Hierarchical Process (AHP) and b) combined AHP and Principal Component Analysis (PCA). From a long list of possible contributors, the model identifies and ranks two key sustainability contributors to the EWM supply chain: overall energy consumption and volume of carbon dioxide generated. Another key finding is a precise time window for policy resurrection, which for the data considered, happens to be 400-600 days from the start of operation. Another interesting outcome is the quality of prediction using a combination of AHP and PCA, which consistently produced better results than any of these ML methods individually implemented. Model outcomes have been verified using a case study to outline a future E-waste sustained roadmap.