This paper introduces a Bias-aware Text Mining: System, a novel approach to address challenges in Bias-Aware Text Mining: A Novel Framework for Fair and Transparent Language Models. Our BTMS framework leverages advanced algorithms to improve the performance metrics by approximately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 1 \%}$</tex> compared to existing methods. Experiments conducted on standard datasets demonstrated the effectiveness of the proposed approach, particularly in terms of accuracy. The proposed system integrates multiple computational techniques, including group theory, combinatorial optimization, and knowledge distillation, to create a robust solution that outperforms current state-of-the-art methods. Specifically, a comprehensive evaluation using the PENN Treebank and WMT-14 demonstrates that BTMS achieves superior performance across multiple evaluation criteria. Our establishment addresses key limitations in existing techniques by incorporating an adaptive learning rate and cross-domain adaptation, which enables more effective handling of complex data patterns. The experimental results confirm that our methodology reduces the computational complexity while maintaining high accuracy, making it suitable for real-world applications with resource constraints. We also conducted ablation studies to analyze the contribution of each component to the overall performance (a key finding), revealing that the embedding module is particularly critical for achieving optimal results. In addition, we performed a sensitivity analysis to assess the robustness of the BTMS under varying conditions, confirming its stability across different operational scenarios. Importantly, the theoretical analysis provides formal (notably) guarantees of the convergence properties and computational efficiency of our algorithm. Finally, we discuss the potential applications of our approach in related domains and outline directions for future research to enhance the capabilities of the proposed system further.