<![CDATA[This study examines the integration of Machine Learning (ML) with Web Intelligence (WI) as a transformative approach for enhancing web-based search and recommendation systems. The objective is to utilize the combined strengths of ML and WI to significantly increase the accuracy, precision, and relevance of predictions, providing personalized and context-aware results that adapt in real-time. Employing a hybrid model that leverages both the predictive capabilities of ML and the dynamic adaptability of WI, this research methodologically assesses the performance against traditional models through rigorous testing. Results indicate that the integrated system substantially outperforms conventional models, demonstrating enhanced performance metrics across accuracy, precision, and recall. Theoretically, this integration contributes to the advancement of WI frameworks, while practically, it offers significant improvements for real-world applications, especially in optimizing user interactions and satisfaction. However, the study also recognizes limitations related to the scalability of the data and models used. Future research should focus on refining model complexity and enhancing real-time data processing capabilities. Additionally, the integration of these technologies supports several Sustainable Development Goals (SDGs), particularly Goal 9 (Industry, Innovation, and Infrastructure) by promoting sustainable industrialization through advanced technologies, Goal 8 (Decent Work and Economic Growth) by fostering economic growth and employment in the tech sector, and Goal 12 (Responsible Consumption and Production) by enabling more informed consumer choices through better recommendations. These connections underline the role of innovative technologies in achieving sustainable development and enhancing global economic and social frameworks.]]>
