Artificial intelligence (AI) is a prominent force in the twenty-first century. Organizations have more data than ever before, therefore it's critical that the analytics team distinguishes between Interesting Data and Useful Data. "Feature Selection" and "Feature Extraction" are two critical parts of Machine Learning. We are now witnessing the emergence of the fourth industrial revolution, as well as a significant number of evolutionary changes in machine learning methodologies to achieve operational excellence in the operation and maintenance of industrial assets in an efficient, reliable, safe, and cost-effective manner. Knowledge-based systems, expert systems, artificial neural networks, genetic algorithms, fuzzy logic, case-based reasoning, and any combination of these approaches are examples of AI methodologies (hybrid systems), machine learning, and biomimicry, such as swarm intelligence and distributed intelligence, are being employed by multidisciplinary researchers to tackle a wide range of previously intractable challenges related with proactive maintenance management of industrial assets. The purpose of this study is to examine the function of artificial intelligence in the condition monitoring and diagnostic engineering management of modern engineering assets. The study also emphasizes the dangers of unethical and immoral usage of AI.