With the aim of promoting international collaboration and providing incentives for stakeholders to accomplish the Global Goals, also known as SDGs, in general and organic residuals reduction in particular, this work offers a thorough and inclusive framework. A method is also offered in this work to categorize countries based on the unsupervised machine learning k-means methodology to expand our knowledge of the differences between nations beyond simply scoring their accomplishment of the 17 SDGs. This is where the originality resides. A clustering analysis, which includes the 17 SDGs, was used in the first scenario as well as in the Household Organic Residues Indicator. All 17 SDGs were considered for the clustering analysis of the second scenario. In the results, three clusters were formed in the first scenario. One of these clusters, named the “High Sustainability” group, distinguished itself by having the top marks among 15 SDGs. Moreover, it performed worse than two other Sustainable Development Goals: SDG 13, which emphasizes climate action, and SDG 12, which promotes responsible production and consumption. For the other case, four country groups were distinguished. The cluster categorized as medium–high sustainability showed the maximum levels of domestic organic waste, whereas the group labeled as “High Sustainability,” with top values across the SDGs, reported the minimum generation of domestic organic residuals.