This study aims to develop a web-based system capable of analyzing truck driver profiles by implementing the Decision Tree algorithm and integrating the Gemini model powered by artificial intelligence (AI). The system is designed to assist logistics companies in identifying driver characteristics and competencies to improve operational efficiency and driving safety. The data used includes technical and behavioral attributes of drivers, which are then classified using a decision tree approach. The test results show that the model achieved an accuracy rate of 64%, which, although not yet high, still demonstrates significant potential in the initial classification process based on the available dataset. The model’s performance is enhanced by Gemini’s ability to capture non-linear patterns through a Deep Neural Network and generate data-driven recommendations. Thus, the integration of Decision Tree and AI provides a predictive approach that can support driver selection and training processes in a more objective and measurable manner.