A Markov model is a statistical model that predicts future states based on the current state, without considering past states. It operates on the principle of memorylessness, meaning that the next state depends only on the present state and not on how it arrived there. This makes it useful for various applications, such as weather forecasting and speech recognition. In a Markov model, states are represented as nodes, and transitions between states are represented as edges with associated probabilities. These probabilities indicate the likelihood of moving from one state to another. By analyzing these transitions, we can make informed predictions about future events or behaviors.