Intelligent Information Retrieval (IIR) has evolved as a critical interdisciplinary field situated at the intersection of information science, artificial intelligence, cognitive science, and human–computer interaction. Unlike traditional information retrieval systems that primarily emphasize algorithmic efficiency and document matching, IIR focuses on embedding various forms of intelligence into retrieval processes, including user modeling, semantic understanding, adaptive interaction, and contextual reasoning. This article presents an extensive, theoretically grounded, and publication-ready examination of IIR, drawing strictly from foundational and contemporary scholarly works in the field. The study revisits classical cognitive and interaction-based models of information retrieval, explores the philosophical and technical debates surrounding the notion of “intelligence” in retrieval systems, and critically analyzes modern semantic, ontology-based, and AI-driven approaches. Through a descriptive and analytical methodology grounded in literature synthesis, the article identifies persistent challenges in aligning system intelligence with human information-seeking behavior. The results highlight that true intelligence in information retrieval emerges not solely from advanced algorithms, but from the dynamic co-construction of meaning between users and systems. The discussion elaborates on theoretical implications, design trade-offs, system limitations, and future research directions, particularly in the context of digital libraries and semantic web technologies. By offering a deeply elaborated and integrative perspective, this article contributes to a more nuanced understanding of IIR as a socio-technical endeavor rather than a purely computational problem.