A Markov Chain is a mathematical system that undergoes transitions from one state to another within a finite or countable number of possible states. It is characterized by the property that the next state depends only on the current state and not on the sequence of events that preceded it, known as the Markov property. This makes Markov Chains useful for modeling random processes in various fields, such as economics, genetics, and computer science. In a Markov Chain, each state has a set of probabilities that determine the likelihood of moving to other states. These probabilities can be represented in a transition matrix, which provides a clear overview of how the system behaves over time. Markov Chains can be classified as either discrete or continuous, depending on whether the state changes occur at distinct time intervals or continuously.