<![CDATA[Waste management in Indonesia faces extreme regional disparities, ranging from critical waste accumulation zones and circular economy transition zones to specific material deficit zones. The primary problem lies in the inability of conventional systems to process heterogeneous waste efficiently, which leads to the failure of sustainable environmental conservation. An intelligent solution is required to integrate physical technology with an adaptive policy evaluation system. This research develops a systematic framework for the development and evaluation of waste processing automation technology. The research stages begin with a Bibliometric Analysis and Systematic Literature Review (SLR) using metadata from Scopus and Web of Science to identify global trends via VOSviewer. Furthermore, this study integrates AI and IoT as primary instruments for nature conservation. Through the processing of large data volumes (Big Data) from IoT sensors, AI (such as Multi-Criteria Decision Making) performs predictive analysis to automatically evaluate three regional conditions. AI plays a crucial role in determining corrective actions, including optimizing the use of oxy-hydrogen (HHO) fuel in incinerators to suppress emissions and managing cross-regional waste logistics, thereby ensuring natural resources are preserved through precise and low-pollution waste elimination processes. This research generates intelligent governance patterns and actionable insights to guide system users, particularly local governments and industrial managers, in implementing appropriate waste processing technologies. This solution provides automated operational guidance that ensures energy efficiency and economic sustainability while maintaining ecosystem preservation through standardized waste processing based on the specific regional characteristics in Indonesia.]]>
