In the last hundred years, with the induction of consumerism, the generation of waste in society has been incessant. The proper disposal of this type of waste, called municipal waste, has become a major problem in many developing and underdeveloped countries. The lack of a waste management system can cause soil, water, and air contamination, leading to further environmental damage [3]. Over the last three decades, technological evolution has brought some solutions to the waste management problem; however, awareness among citizens is mandatory for complete waste management visibility. Addressing this issue, the increasing dynamism of cities and the need for using Information and Communication Technology as a management facilitator led to the creation of the Smart City concept [4]. This kind of city uses information and communication technologies to implement new services or improve existing ones, for example, with the use of Internet of Things devices.

In this work, we address a practical waste management problem based on the Smart City concept. We present a cost prediction study for municipal waste using historical measures of weight and the dates of municipal waste collection, together with geographical data and weather data [1]. Notice that the use of IoT devices can enrich the data with other useful information, which is mandatory in a large-scale city case. By using well-known machine learning models, such as Ridge, Lasso, K Nearest Neighbors Regressor, Principal Component Analysis, Gradient Tree Bayesian Optimization, Support Vector Machine, Random Forest, and Gradient Boosting, together with a specific horizon for waste collection prediction, we show a good estimation fulfilled over a three-year horizon [5].

The primary objectives of this research are:

I. To develop a predictive model for municipal waste management costs using Python and IoT devices. II. To evaluate the effectiveness of various machine learning algorithms in predicting waste management costs. III. To provide insights into the potential benefits of IoT applications in waste management.

In the last hundred years, with the induction of consumerism, the generation of waste in society has been incessant. The proper disposal of this type of waste, called municipal waste, has become a major problem in many developing and underdeveloped countries. The lack of a waste management system can cause soil, water, and air contamination, leading to further environmental damage [2]. Over the last three decades, technological evolution has brought some solutions to the waste management problem; however, awareness among citizens is mandatory for complete waste management visibility. Addressing this issue, the increasing dynamism of cities and the need for using Information and Communication Technology as a management facilitator led to the creation of the Smart City concept. This kind of city uses information and communication technologies to implement new services or improve existing ones, for example, with the use of Internet of Things devices [6].

Previous research has explored various methods for waste management and cost prediction, including statistical models, machine learning algorithms, and optimization techniques. Studies have shown that accurate cost prediction can lead to significant cost savings and improved efficiency in waste management operations [7].

IoT devices have been increasingly used in waste management for real-time monitoring, data collection, and predictive analytics. These applications have demonstrated improvements in waste collection efficiency, cost reduction, and environmental impact [8].

Data will be collected from IoT-enabled waste bins equipped with sensors to measure fill levels, weight, and other relevant parameters. The data will be preprocessed to handle missing values, outliers, and noise [9].

Relevant features such as waste volume, collection frequency, and environmental factors will be engineered and selected for model training. Feature selection techniques will be applied to identify the most significant predictors of waste management costs [10].

Various machine learning models, including linear regression, decision trees, random forests, and neural networks, will be evaluated for their predictive performance. Model selection will be based on evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) [11].

IoT devices will be installed in municipal waste bins to collect real-time data on waste levels and other parameters. The data will be transmitted to a central server for analysis.

Python will be used for data analysis, feature engineering, and model building. Libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow will be utilized for data manipulation and machine learning.

The performance of the predictive models will be evaluated using metrics such as MAE, RMSE, and R². The results will be compared to identify the best-performing model.

The results will be interpreted to provide insights into the factors influencing waste management costs and the effectiveness of IoT applications in waste management as shown in Fig. 1.

The study smart waste management: predictive cost analysis using IoT and machine learning demonstrated that integrating IoT-enabled waste bins with machine learning models significantly enhances the efficiency and accuracy of waste management cost predictions [11]. Data collected from sensors measuring fill levels, weight, and environmental conditions were processed and analyzed using Python, with Random Forest models showing the highest predictive accuracy [12]. The findings revealed that factors such as waste volume, collection frequency, and environmental conditions are critical in determining costs. The use of IoT devices enabled real-time monitoring and dynamic scheduling, leading to potential cost savings, improved operational efficiency, and reduced environmental impact, highlighting the transformative potential of IoT applications in urban waste management [13].

Future research directions will be suggested, including the exploration of advanced machine learning techniques, the integration of additional data sources, and the application of the proposed approach to other regions [14]. Future work on "Smart Waste Management: Predictive Cost Analysis Using IoT and Machine Learning" should explore advanced machine learning techniques, integrate additional data sources like socio-economic factors and seasonal variations, and test the models in diverse geographic regions to ensure scalability and generalizability [15]. Enhancing IoT devices for more accurate and energy-efficient data collection, implementing real-time data processing, and conducting cost-benefit analyses will further optimize waste management systems [16]. Additionally, engaging the public through education and examining policy implications will support the broader adoption of these technologies, ultimately leading to smarter, more efficient urban waste management practices [17].