Amidst a surge in production equipment accidents, posing grave societal and industrial ramifications, the imperative to fortify preventive measures and safety protocols grows pivotal. Learning from accident cases, their lessons, and preventive strategies assumes paramount significance to enhance the inherent safety of petrochemical enterprises. The well-established Hazard and Operability (HAZOP) analysis method has been instrumental in identifying risks and averting accidents within the petrochemical sector. However, the reservoir of insights embedded in completed HAZOP reports often remains untapped and underutilized. Moreover, the assessment of novel processes leans heavily on expert’s intuition, a subjective and time-intensive endeavor. This has prompted a push for automation using Natural Language Processing (NLP) and Machine Learning (ML). The present work introduces an intelligent HAZOP analysis methodology, underpinned by data mining principles. This innovative approach offers adaptability to modest-sized units and companies with limited size of data, and provides the foundational automated framework for identifying risks, preventing accidents, and facilitating emergency response in petrochemical facilities.