This chapter provides a structured overview of Monte Carlo Methods (MCMs) and their expanding role in artificial intelligence. It introduces the fundamental principles of stochastic sampling, probabilistic modeling, and simulation, along with core Monte Carlo algorithms used for optimization, inference, learning, and decision-making under uncertainty. It traces the evolution of these techniques from classical scientific computing to their growing relevance in AI. Emphasis is placed on conceptual understanding and on the role of MCM approaches in addressing complex, data-driven problems. By connecting the progression from foundational theory to contemporary AI contexts, the chapter aims to offer readers a coherent perspective on their importance in enabling robust, scalable, and computationally efficient intelligent systems.