Markov decision processes is a mathematical framework for modeling sequential decision-making in stochastic environments. It provides a rigorous method for analyzing situations where outcomes are probabilistic and influenced by chosen actions. This framework characterizes dynamic systems by states, available actions, state transition probabilities, and associated rewards, adhering to the Markov property where the next state depends solely on the current state and action. Its significance lies in enabling the computation of optimal policies aimed at maximizing cumulative expected rewards, making it fundamental in fields like reinforcement learning, control theory, and operations research.