End-of-life (EoL) chemical flow analysis (CFA) is essential for understanding potential environmental fate and exposure pathways and supporting cost-effective circular economy routes of chemicals. This study presents a multi-scale computational framework that integrates data engineering, data-driven modeling, and process systems engineering (PSE) methods to support CFA during EoLduring EoL stage. Using methyl methacrylate (MMA) as a case study, the framework examines the EoL supply and management chain for plastic-related chemicals, leveraging publicly available regulatory data to identify potential chemical redistribution, environmental releases, and EoL exposure scenarios. The analysis identifies potential inter-EoL transfers, where chemicals transition between waste management processes before final disposal or reuse, potentially leading to unintended environmental releases. The presence of other chemicals detected alongside MMA in waste streams (co-occurring chemicals) suggests the possibility of contamination in recycled materials, unintentional emissions into the environment during recycling processes, and releases from wastewater treatment systems. However, data limitations and reporting variability introduce uncertainties that may affect tracking accuracy. To address these challenges, this study underscores the need for integrating facility, process, and equipment-level data to refine release estimates and exposure assessments. Future research should explore hybrid modeling approaches, combining top-down regulatory data with bottom-up process insights, and leverage graph-based methods for supply chain simulation. Advancing data-driven CFA methodologies can provide a science-based foundation for regulatory oversight, circular economy strategies, and sustainable chemical management.