<p>Aluminum alloys throughout the last century have experienced extensive development, owing to their unique strength-to-weight ratio. This led to generating multiple alloy grades. However, large number of grades present challenges when it comes to the recycling of aluminum scrap, which is the current and future trend in aluminum alloy production and application. Therefore, there is an urgent need to decrease the number of alloying grades while preserving their performance. In this study, we designed an optimization loop based on Machine Learning (ML) and material science knowledge for the 292 sets of data collected on 42 grades of 6xxx series aluminum alloys, focusing on their mechanical, service, and technological properties under T5, T6, and T7 tempering conditions. K-means clustering and principal component analysis algorithms were applied to form various clusters of alloys and are further re-clustered into fine sub-clusters. An optimal alloy (OA) for each sub-cluster was identified based on optimization criteria. After successive iteration, we were able to reduce 42 grades of the 6xxx series into a set of 10 OA’s each performing optimally. This method not only support the capability of machine learning in selecting OA’s but also introduce a future direction for recycling practices in the aluminum industry.</p> Graphical Abstract <p></p>