A reversible Markov chain is a type of Markov chain where the process satisfies the detailed balance condition, meaning the probability of transitioning from state i to state j is the same as transitioning from j to i when weighted by the stationary distribution. This property makes reversible Markov chains particularly useful in simulating equilibrium distributions, such as in the Metropolis-Hastings algorithm used in Markov Chain Monte Carlo methods.