This study explores the application of the Random Forest algorithm for classifying dominant team roles based on Belbin’s Team Roles Self-Perception Inventory. A synthetically generated dataset of 100,000 instances was used to train and evaluate the model, ensuring that the distribution of team roles reflects real workplace environments. The results demonstrated high classification accuracy, with an overall precision of 0.98725. The study found that roles with distinct behavioral characteristics, such as shaper and coordinator, were easier to classify, whereas roles with overlapping traits, such as monitor evaluator and plant, posed greater challenges. The findings have significant implications for modern workforce management. By utilizing machine learning for team role classification, organizations can optimize team formation, enhance collaboration, and improve productivity. The model offers a data-driven approach to aligning employees with roles that match their strengths, reducing subjective decision-making in human resource processes. Despite its effectiveness, the study acknowledges limitations, such as the reliance on synthetic data and the need for further validation using real-world datasets. Future research should explore deep learning models and integrate natural language processing techniques for improved role prediction. This study highlights the potential of AI-driven HR analytics to create balanced, high-performing teams in contemporary business environments.