The increasing integration of renewable energy into electricity markets has been accompanied by high levels of uncertainty and variability in generation and demand, highlighting the need for an innovative model capable of simulating locational marginal prices (LMPs) while considering the new market characteristics. While many developed countries have implemented advanced machine learning-based simulation methods, numerous developing countries, such as Mexico, still rely on traditional models. The objective of this research is to introduce an intelligent model for simulating LMPs in the Mexican electricity market. The proposed model implements an approach based on fuzzy subtractive clustering and particle swarm optimization, achieving accurate simulations with low computational complexity. Although its effectiveness is limited to the market for which it has been trained, the proposed model stands out not only for its precision and ability to handle variability and uncertainty but also as one of the first applications of this type in the Mexican electricity market.