<![CDATA[Indonesia's waste generation increased from 28.59 million tons per year in 2021 to 34.21 million tons per year in 2024, a 19.67% increase. However, in 2024, only 46.1% of the total waste generation was successfully managed.. This condition highlights the need for a more efficient waste management solution, particularly at Temporary Disposal Sites (TPS), which still rely on manual monitoring and often experience waste overflow. This study aims to develop a Smart Environmental System based on the Internet of Things (IoT) and Machine Learning to monitor waste levels in real time and predict disposal patterns using historical data. The research uses a qualitative approach through field observations, interviews with the Environmental Agency, and literature studies to identify system requirements. System design was carried out using UML diagrams, followed by the development of an IoT device using ESP32 and an Android application built with Flutter, integrated with Firebase. The Machine Learning model employs the Random Forest algorithm to classify waste-level conditions. System testing included unit testing, integration testing, performance testing, and user evaluation using the PIECES method. The results show that the Performance, Information, Control, and Efficiency aspects scored above 80%, indicating that the system effectively provides sensor information, ensures data security, and improves operational efficiency. However, the Economic and Service aspects still require optimization, particularly in reducing operational costs and improving system maintenance routines. Overall, the system demonstrates strong potential in supporting smarter, faster, and more efficient waste management, and is suitable for further development.]]>
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