MSW management is one of the critical global challenges, especially in the context of rapid urbanization and economic development. This study examines the factors that influence MSW generation and the potential of machine learning techniques to predict future trends. Using a dataset covering 196 countries and 13 regions, we analyze key variables such as population, GDP per capita, urbanization rate, and Human Development Index (HDI). In preprocessing, missing values were managed and new features like MSW and population growth rates, waste intensity, and HDI-adjusted waste were engineered to capture the complex relationships influencing MSW generation. An Extra Trees Regressor model was used for future prediction, achieving an R² value of 0.89, thereby proving its excellent performance in predictive results. Our results demonstrate how economic development and urbanization contribute to waste generation trends, with the AI models further providing insightful implications of such changes. The need for data-informed approaches for designing sustainable systems of waste management is underscored by this study, highlighting that socio-economic drivers and technological leaps must be used in concert with each other in order to adequately address the challenges of increasing wastes. The findings offer a basis for future policies and actions that would aim at managing waste effectively in an increasingly urbanized world.

Geospatial analysis plays a very crucial role in predicting environmental changes, especially in the context of CO2 emissions and climate forecasting. This study uses continent-wise geospatial analysis to assess the potential patterns and implications of CO2 emissions by 2100, using ML techniques. The objective of this research is to spatially integrate the data with ML models to predict the geographical distribution of CO2 emissions at regional levels, thereby ascertaining which factors are predominant in influencing rates of emissions. For this purpose, the world is stratified into continents so that fine-grained regional differences in trends of emissions may be understood, keeping in mind aspects such as population density, industrialization, energy consumption, and climate policies. These are regression, classification, and neural networks that the machine learning algorithms use on satellite data, demographic information, and economic indicators to predict future emissions. This helps explain those hotspots in the first place, regions of high vulnerability to impacts from emissions, and opportunities for targeted mitigation strategies. Ultimately, this study provides a basis for understanding the geographical dynamics of CO2 emissions and more effective strategies by policymakers and environmental researchers to achieve emission reduction and climate change mitigation in the following century.

The continuous rise in CO₂ emissions is a major contributor to climate change, affecting ecosystems, economies, and public health. Predicting future emissions accurately is crucial for designing effective policies and mitigation strategies. This study explores multiple machine learning models for CO₂ emissions forecasting, comparing traditional methods like Support Vector Machines (SVM), Linear Regression, and Decision Trees with advanced deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Multi-Layer Perceptron (MLP). Using a time-series approach, we forecast emissions up to 2100 and assess model performance through key metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² score. Our results indicate that deep learning models, especially LSTM and GRU, outperform traditional methods in capturing complex patterns and trends in emissions data. Additionally, we generate geospatial visualizations to highlight regions facing the highest risks. These insights provide valuable guidance for policymakers and environmental researchers, enabling data-driven decisions for emission reduction, resource allocation, and long-term sustainability planning in the fight against climate change.