Stochastic processes are mathematical frameworks used to model systems that evolve unpredictably over time, influenced by inherent randomness. They are essential in probabilistic AI for representing uncertainties, predictive modeling, and decision-making in dynamic environments, such as speech recognition, financial forecasting, and robotic navigation, allowing AI systems to manage and learn from unpredictable data sequences effectively.