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The focus of this research is on addressing the inefficient management of Municipal Solid Waste (MSW), which often results in large quantities of waste being dumped into garbage bins. Previous approaches have struggled to effectively manage MSW promptly. To improve this process, we propose a time-series-based, energy-efficient waste prediction model. Initially, we convert the Bins Historical Time Series Dataset into a Comma-Separated Values (CSV) file format. The model, named Exponential Auto-Regressive Integrated Moving Average-Nesterov-accelerated Adaptive Moment estimation-Long-Short Term Memory (EARIMA-LSTM) using NADAM optimizer, is trained and tested using numeralized features extracted from the dataset. Smart bins equipped with Internet of Things (IoT) technology are then deployed to predict waste levels using the EARIMA-NADAM-LSTM model. After waste level detection, we …