Authors - Antonio Cortes Castillo Abstract - The rapid advancement of Artificial Intelligence (AI) has fundamentally transformed data center operations. The widespread adoption of AI services has introduced new requirements for hosting AI systems, prompting significant modifications in the design and construction of modern data centers. As a result, data center operators must implement comprehensive strategies to address the challenges associated with AI integration. This study explores the application of machine learning techniques using neural networks and Multilayer Perceptron (MLP) models for data center optimization. The research focuses on critical metrics, including power consumption, liquid cooling, fiber-optic systems, and the number of fiber-optic routing paths, which pose significant challenges for data center operators. Experimental results are analyzed using simulation tools, including SPSS, to demonstrate enhancements in data center performance and efficiency.
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