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. This memoryless property is known as the Markov property. Markov chains are widely used in various fields, including statistics, economics, and computer science. They can model a range of processes, such as predicting weather patterns, analyzing stock market trends, or even generating text. The simplicity and effectiveness of Markov chains make them a valuable tool for understanding complex systems.