AI-Powered Energy Optimization for Carbon Emission Reduction and Sustainability

Smart energy and lower carbon emissions are the paths to global survival. Standard approaches often lack real-time comprehensive data on economic and population trends, making resource utilization challenging. This study utilizes well-established datasets to bridge this gap. International energy consumption patterns are examined while watching CO2 emissions in relation to GDP numbers and population statistics using a machine learning system. From our analysis, we confirm that the K-Mean clustering method divided countries into three categories, which achieved strong validation when measured using a silhouette score of 0.9565 and indices of 0.5041 and 49136.87 from Davies Bouldin and Calinski Harabasz, respectively. DBSCAN and hierarchical clustering are tested against K-means, and we found K-means to produce more effective groups that do not allow data clusters to be close to each other. Heatmaps and 3D scatter plots were used as visual tools to show the main differences between groups. While densely populated and economically advanced clusters constituted the great majority of emissions, smaller clusters presented their own particular development challenges. Using AI cluster analysis, our research aids decision-makers in identifying methods to save energy and limit pollution better. Relying on data to inform policy solutions that help advance the United Nations Sustainable Development Goals, this research takes a practicable approach.