Personalized learning is a foundational element of effective education, aiming to tailor teaching methods to individual learners’ needs. Central to this approach is understanding learners’ personalities, as it can significantly influence their learning preferences and outcomes. In this paper, we propose a novel method for predicting learner personality. Our method leverages the Linear Support Vector (Linear SVC) classifier technique and the Myers-Briggs Type Indicator (MBTI) model to predict learner personalities. We utilize the PersonalityCafe dataset for this purpose, employing natural language processing (NLP) preprocessing techniques and the TfidfVectorizer for feature extraction. The experimental results reveal the effectiveness of the linear SVC, achieving an impressive accuracy of 84% in predicting MBTI personality types among learners. By accurately predicting learner personality traits, our method enhances the personalization of learning experiences, leading to improved learning outcomes and engagement.