Artificial Intelligence (AI) is redefining how we live, learn, and interact. In education, this transformation is particularly pronounced, as students rapidly adopt AI tools, chatbots, and virtual assistants to support their learning, while educators and institutions strive to keep pace. This study investigates the antecedents of AI adoption among business students in Bangalore, India, with a focus on identifying the key psychological and contextual factors influencing students’ behavioral intention to adopt AI technologies. The antecedents were selected based on established technology adoption frameworks, including constructs such as facilitating conditions, social influence, perceived ease of use, perceived usefulness, trust, and risk. Nineteen predictor constructs were initially identified, and Principal Component Analysis (PCA) was applied to reduce dimensionality and uncover underlying patterns and correlations among these variables. These constructs were then modelled against the target variable, behavioral intention to adopt AI. To enhance predictive accuracy and interpretability, a machine learning approach was employed. Five algorithms—Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, were compared. Among these, the Decision Tree classifier demonstrated the highest predictive performance based on standard evaluation metrics. The model revealed that facilitating conditions, social influence, perceived ease of use, and perceived usefulness were the most influential predictors of AI adoption intent among students. Key insights emerged from the students’ responses. Students expressed the need for institutional resources and support to effectively utilize AI tools, highlighting the importance of creating conducive conditions. Social influence also played a critical role, with many respondents agreeing that people important to them, such as peers and instructors, encouraged the use of AI. Perceived ease of use and usefulness were reflected in students’ emphasis on clarity, efficiency, and task accomplishment through AI tools. Additionally, while students were generally positive toward AI, concerns related to data security and privacy risks were evident, indicating a level of caution in their adoption behaviour. These findings offer practical implications for integrating AI tools into academic settings. By addressing resource gaps, strengthening peer and faculty-led encouragement, and enhancing the usability and transparency of AI tools, educational institutions can foster greater acceptance and effective use of AI in learning. This research thus provides a data-driven foundation for advancing AI integration strategies within higher education.