“Comparative Analysis of AI Models and Traditional SEO Techniques Using Real-World Survey and Performance Data”

This study presents a comparative analysis between Artificial Intelligence (AI)-based models and traditional Search Engine Optimization (SEO) techniques using real-world survey and performance data. The research integrates user behavior insights and technical SEO metrics to evaluate the effectiveness of predictive AI models in improving search engine visibility and website performance. A dataset derived from survey responses (n ≈ 130 analyzed; ~1230 collected) and SEO performance indicators is utilized. Machine learning models such as Random Forest and Regression are compared with traditional heuristic SEO methods. The results demonstrate that AI-driven approaches significantly outperform traditional techniques in predicting ranking outcomes, optimizing page performance, and enhancing user engagement. The study contributes to the development of intelligent SEO systems and highlights the practical applicability of AI in modern digital ecosystems.

AI in SEO, Machine Learning, Website Optimization, SEO Prediction, Comparative Analysis, User Behavior Analytics

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