This book explores cutting-edge machine learning and clustering techniques to tackle critical challenges in engineering, environmental science, and sustainability. The book provides an in-depth examination of clustering methodologies, covering unsupervised and supervised techniques, data preprocessing, distance metrics, and cluster validation methods such as the elbow and silhouette techniques. Readers will find practical insights into applying these methods to real-world problems, including clustering greenhouse gas emissions, optimizing energy systems, and analyzing the energy-food nexus in the context of global crises. By integrating theoretical foundations with hands-on applications, this book serves as a valuable resource for researchers, engineers, and professionals seeking data-driven solutions for sustainability challenges.