In the current era of data-driven enterprise administration, the selection of intelligent suppliers is essential for improving operational efficiency and competitiveness. In order to facilitate the evaluation and selection of suppliers for a cleanroom garment manufacturing company, this investigation suggests the implementation of a hybrid framework that combines the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Expert input and structured data analytics were implemented to evaluate six critical criteria: quality, pricing, transportation, service, social responsibility, and environmental impact. The criterion weights were determined using AHP, and the suppliers were ranked according to their proximity to the optimal solution using TOPSIS. The integrated model exhibited a high level of efficacy in determining the most appropriate supplier in the context of numerous business constraints. The results indicate that quality and service are the most influential criteria, with Supplier C being the highest ranked. This decision analytics paradigm establishes a transparent and data-driven foundation for enterprise procurement, thereby reducing decision risk and improving supply chain intelligence. The proposed framework is consistent with the goals of AI-powered business intelligence and can be seamlessly incorporated into enterprise platforms to facilitate automated, scalable decision-making.