A study aimed at improving learners’ efficiency, satisfaction, and the effective sharing of high-quality educational materials through personalized and precise delivery of online resources is underway. The research leverages social media data integration to achieve these goals. Initially, a learner profile is developed by amalgamating social media insights, which includes learners’ fundamental details, areas of interest, and favored subjects. Subsequently, educational content is curated according to these preferences, ensuring the recommended resources are tailored to individual needs and interests. Lastly, a customized and precise recommendation system for online educational resources is devised by analyzing user behavior patterns, personal data, and social network attributes. The results demonstrate that this method achieves a personalized recommendation accuracy rate of over 98%. As a result, it effectively furnishes users with customized and precise educational resource suggestions based on their multifaceted attributes.