This research introduces a hybrid artificial intelligence approach that combines a fuzzy inference system (FIS) with particle swarm optimization (PSO) to guide factory location decisions and energy management strategies in nearshoring scenarios. The primary goal is to utilize a fuzzy inference system to convert qualitative and uncertain factors, such as grid reliability, regulatory incentives, and seasonal fluctuations, into a unified suitability index value. Then, the optimization algorithm, PSO, iteratively optimizes the site selection by minimizing this index, improving both energy efficiency and location effectiveness. Applied to Mexico’s growing nearshoring industry, the framework delivers recommendations for strategically situating manufacturing facilities across diverse policy and energy environments. Simulation results show that the proposed framework can reduce operational disruptions and enhance system resilience, thereby supporting more robust and data-driven energy industrial planning.