The examination of clustering techniques in Process processes Engineering (PSE) demonstrates their significant influence on the optimization of intricate industrial processes. These technologies have shown considerable progress in identifying defects, enhancing renewable energy operations, managing supply chains, and optimizing chemical processes by efficiently handling large datasets, supporting better decisions, and promoting sustainable practices. The integration of clustering with innovative machine learning procedures, as well as real-time data analytics, has broadened its use, enhancing robustness and flexibility in dynamic contexts. Notwithstanding these gains, problems remain, including computing requirements and the necessity for adaptable, real-time clustering frameworks to accommodate changing industrial environments. This work rigorously assesses advanced clustering approaches in PSE, highlighting their strengths, limitations, and prospects. It underscores the significance of cross-disciplinary methodologies and ethical responsibilities when applying clustering techniques to tackle urgent global issues, including sustainability and energy transitions, while delineating a trajectory for their ongoing development and incorporation into next-generation industrial systems.