The growing demand for personalized digital services has accelerated the adoption of artificial intelligence technologies in web applications. Recommendation systems have become a key mechanism for improving user experience by delivering relevant content, products, and services based on individual preferences and behavioral patterns. PHP-based platforms continue to be widely used for developing educational, commercial, and information-oriented web systems, creating opportunities for integrating intelligent recommendation functionalities. This study investigates the development of AI-driven recommendation systems for PHP platforms, emphasizing recommendation algorithms, system architecture, data processing mechanisms, and implementation strategies. The analysis demonstrates that intelligent recommendation technologies can significantly improve personalization, user engagement, and decision-making efficiency while introducing new technical and data management challenges.