The pricing of multi-dimensional options presents significant challenges, as it is a fundamental issue in modern large-scale financial analysis. A European call option grants its holder the right, but not the obligation, to acquire a specified quantity of an underlying asset \(S\) at a predetermined strike price \(K\) on a fixed maturity date \(T\) . In financial modeling under uncertainty, Monte Carlo and quasi-Monte Carlo techniques have emerged as powerful tools for tackling complex valuation problems. This paper investigates the intelligent optimization of simulation-based approaches to accurately determine the fair value of multi-dimensional European options. Monte Carlo methods, particularly in higher-dimensional settings, are well-suited for such pricing tasks due to their flexibility and effectiveness. In this study, we introduce intelligent optimization strategies that leverage low-discrepancy sequences and variance reduction techniques, significantly enhancing the precision of conventional Monte Carlo simulations. These refinements lead to more reliable pricing outcomes, which are critical for informed financial decision-making under uncertainty. Furthermore, the proposed methodology demonstrates robust performance in scenarios where traditional deterministic approaches struggle, such as high-dimensional spaces, intricate contract structures, and dynamically evolving market conditions. By integrating intelligent algorithms into Monte Carlo simulations, this approach contributes to the broader domain of computational finance and decision-making in uncertain environments.