Fredholm integral equations play a crucial role in various scientific and engineering applications, yet their multidimensional forms present significant computational challenges. This paper introduces an intelligent Monte Carlo (IMC) approach for efficiently solving multidimensional Fredholm integral equations. By employing an unbiased stochastic method based on the walk-on-equation (WOE) algorithm for linear systems, the proposed technique enhances computational accuracy while significantly reducing simulation costs. The effectiveness of the approach is validated through benchmark problems, demonstrating its capability to handle high-dimensional integrations with improved convergence rates. Unlike traditional Monte Carlo methods, the IMC technique optimizes sampling efficiency, ensuring a balance between computational feasibility and solution precision. Results indicate that the proposed method provides a scalable and robust solution for solving complex integral equations, making it highly applicable in applied mathematics, physics, and engineering. The study highlights the potential of IMC as a powerful computational tool, offering new possibilities for tackling multidimensional integral equations with enhanced efficiency and reliability.