E-marketing has emerged as a fundamental component of contemporary commercial strategy, utilizing internet channels to advertise items and ser-vices. Nonetheless, conventional e-marketing tactics frequently encounter challenges with accuracy, particularly for individuals with minimal transac-tion history. This study tackles the issue by offering an improved e-marketing approach that combines the Support Vector Machine (SVM) with the Apriori Algorithm to boost predictive accuracy. The approach entails gathering user data from social media (utilizing the Twitter API) and e-commerce platforms, preparing the data using natural language processing (NLP), and employing support vector machines (SVM) for sentiment analysis and classification. The Apriori Algorithm is employed to develop association rules for things that are regularly purchased. Experimental findings indicate that the suggested approach attains an accuracy of 92.1%, a precision of 0.89, and a recall of 0.90, surpassing conventional techniques, particularly for users with restricted transaction histories. The results indicate that the amalgamation of social media data with machine learning methodologies may substantially improve e-marketing efforts, providing enterprises with a more effective and precise instrument for tailored product suggestions.