This review paper presents a comparison between Petri Nets and other analytical techniques such as Markov Models, Monte Carlo Simulations, and Fault Tree Analysis applied for evaluating the Reliability, Availability, and Maintainability (RAM) of complex industrial systems. The most commonly used modelling techniques are Petri Nets (PN), Markov Models, Monte Carlo Simulations, and Optimization-based methods etc. This paper covers forty-three research papers addressing various industrial sectors, such as manufacturing, power generation, chemical, paper, food, and healthcare industries. Petri Nets, especially Generalised and Coloured modelling techniques, have been applied by most of the researchers, which is suitable for modelling of a dynamic, concurrent, and resource-bound system. In the present review, fifteen research studies have been reviewed by the author, where the Markov technique has been applied across different industries, and it has been found that it is better for the analysis of static industrial systems. The major limitation of the Markov technique is its inability to address concurrent modelling and the simultaneous failure of the subsystems. Some of the studies apply Monte Carlo methods to provide probabilistic insights, and although they offered timely insights, they had limited presentation of process structures. The present review shows that the lack of integration of real-time data, modelling human-system interactions, and under/over utilization of resources in high-risk sectors, such as nuclear power generation, aerospace industry, petroleum refineries, and rubber manufacturing, needs to be addressed. The paper concludes by emphasizing the need for a hybrid, intelligent, and domain-specific RAM model to support decision-making, accurate prediction, timely monitoring, and the reliable reactive nature of complex systems.