Study and Design of AI-Driven Models for Enhancing Search Engine Visibility and Website Performance Optimization: A Survey-Based Approach

This research focuses on the study and design of Artificial Intelligence (AI)-driven models for enhancing search engine visibility and optimizing website performance. The study adopts a survey-based quantitative approach, supported by an extensive literature review, to analyze user behavior, website performance expectations, and awareness of AI applications in Search Engine Optimization (SEO). Data collected from more than 1250 respondents reveal that website loading speed, navigation, and mobile friendliness are the most influential factors affecting user experience and search rankings.

The findings indicate that AI-based techniques such as automated keyword analysis, semantic content optimization, learning-to-rank models, and predictive performance analytics can significantly improve SEO outcomes and website efficiency. Based on empirical insights and literature synthesis, this study proposes an AI-driven integrated framework combining machine learning, natural language processing, and performance monitoring to enhance search visibility and user engagement. The research contributes by bridging the gap between AI-based SEO strategies and website performance optimization within a unified model.

Artificial Intelligence, Search Engine Optimization (SEO), Website Performance Optimization, Machine Learning.

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