This paper presents a machine learning-based approach for predicting personality traits from textual data. The study leverages the Big Five Personality dataset, which consists of users’ Twitter content along with corresponding personality type labels. Our methodology encompasses multiple stages, including data preprocessing, feature extraction, model training, and evaluation. Various machine learning algorithms have been explored to determine the most effective approach for personality prediction, including AdaBoost, Gradient Boosting, XGBoost, CatBoost, and Random Forest classifiers. The primary objective of this research is to develop an accurate system capable of inferring a user’s personality traits from their textual data. Such a system holds significant potential for a wide range of applications, including personalized marketing, targeted content recommendations, user profiling, and psychological research. By analyzing linguistic patterns and behavioral cues within textual content, this work aims to enhance the understanding of personality prediction through computational methods while contributing to advancements in natural language processing and artificial intelligence.