The rapid expansion and deployment of AI technologies in various sectors have raised critical concerns about fairness, bias, and ethical implications. Addressing these concerns, the novel integration of structural fairness mechanisms and active learning frameworks into the Mistral Large model presents a significant advancement in creating AI systems that are both highly accurate and equitable. The proposed approach involves enhancing the model architecture with fairness-aware layers and incorporating a modified loss function that penalizes biased predictions, thereby promoting equitable outcomes across diverse demographic groups. Additionally, the implementation of an active learning framework prioritizes the selection of the most informative data points, which not only improves model accuracy but also maintains fairness throughout the learning process. Experimental evaluations demonstrate substantial improvements in performance metrics such as accuracy, precision, recall, and F1-score, alongside significant reductions in bias as measured through disparate impact and equal opportunity difference. The results underscore the feasibility and effectiveness of embedding fairness considerations into the core design and training processes of AI models, paving the way for the development of robust LLMs that adhere to ethical standards and promote social justice. The methodologies and findings provide a comprehensive framework for future research and practical implementations aimed at achieving both excellence and fairness in AI systems.