INTELLIGENT SEARCH ENGINE OPTIMIZATION AND RETRIEVAL FRAMEWORK USING MACHINE LEARNING ALGORITHMS

Abstract

The rapid growth of digital information has created a demand for smarter and more efficient search engines. Traditional search systems that rely solely on keyword matching and static ranking often fail to capture user intent or contextual meaning. To overcome these limitations, this study introduces an Intelligent Search Engine Framework that leverages machine learning algorithms for enhanced information retrieval and ranking. The proposed framework utilizes supervised and unsupervised learning methods to improve query interpretation, semantic understanding, and result optimization. Techniques such as feature extraction, clustering, and ranking model training are incorporated to enable adaptive learning from user interactions. Experimental evaluation shows that integrating machine learning significantly improves retrieval precision and user satisfaction compared to conventional keyword-based search engines. The framework demonstrates the potential of intelligent algorithms to transform traditional search systems into adaptive, context-aware, and user-centric platforms for efficient information access.