Waste management systems remain far from meeting material recovery targets and net-zero decarbonization goals. Their design is inherently challenging because it requires resolving competing environmental, economic, and social objectives across tightly coupled process, control, and regulatory domains, under substantial uncertainty in waste composition, product quality, and market outlets. This lack of integration continues to limit the credibility and practical use of decision-support methods for waste management. In this contribution, we define six technical requirements for a decision-support framework suited to industrial research and development and introduce a tool designed to meet them. The proposed Decision-Support Platform couples Modelica-based process simulation with Python workflows for data handling, life-cycle inventory generation, sustainability assessment, and results analysis. It is specifically designed to support multi-domain, time-dependent modeling of heterogeneous waste systems while ensuring modularity, transparency, and traceability of both models and data. In this way, the platform addresses a persistent methodological gap between detailed process-system representation and life-cycle sustainability assessment of Waste-to-X systems. We demonstrate the platform through a case study of municipal solid waste treatment integrating incineration with energy recovery and optional manual and automatic sorting technologies. Results show that automatic sorting operated at nominal throughput yields the lowest environmental impacts, reducing human health, ecosystem quality, and global warming potential indicators by 31 to 39% relative to the incineration-only reference. Manual sorting, in contrast, does not generate substantial economic gains and shifts burdens in the environmental and social indicators.