The exponential growth of digital content has driven the need for more intelligent, context-aware information retrieval systems. While traditional keyword-based search engines remain foundational, they often fall short of capturing deeper semantic meaning. This article explores the evolution, methodologies, and recent developments in intelligent information retrieval systems powered by artificial intelligence. Special attention is given to the use of machine learning, natural language processing (NLP), and neural networks to improve relevance, personalization, and contextual understanding, including the application of learning-to-rank techniques. The paper contrasts the strengths and limitations of conventional search technologies with those of AI-driven models. A critical part of the study focuses on potential risks associated with AI-based search engines, including environmental concerns linked to the heavy water consumption of data centers relying on water-based cooling systems. The research concludes that a holistic approach is needed in the design and implementation of AI-powered search systems—one that integrates ethical, cognitive, and environmental considerations. This article will be of interest to professionals in media and information technology, researchers, and developers engaged in building intelligent search infrastructures.

information, artificial intelligence, search system, environmental risks

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