Stochastic complex systems are composed by a large number of seemingly simple variables that exhibit non-linear interactions with each other, causing the emergence of complexity and non-deterministic dynamics in the edge between order and chaos. Hence, the evolution of these systems seems to be completely out of control, with unpredictable behaviors. In this paper, using information geometry as a mathematical approach to chaos and complexity, we investigate how information theory can be used to analyze the dynamics of pairwise Ising random fields along Markov Chain Monte Carlo simulations in which phase transitions are observed. Our experiments indicate that Fisher information regarding the inverse temperature parameter can bring important insights, since it signalizes changes in the global spatial dependence structure. Information-theoretic curves are built to show that, despite the random nature of the system, it is possible to identify an asymmetric pattern of evolution when the system moves towards different entropic states.