Markov chains represent an essential mathematical tool for describing the transition from one state to another within a system. This model is based on a fundamental characteristic: the absence of memory. In other words, the probability of moving to a future state is determined solely by the current state, independently of the previous path. This concept is widely used in various fields such as economics, social sciences, and computer science, particularly for modeling stochastic processes where future events are conditioned exclusively by the present.