Named Entity Recognition (NER) is a crucial component in extracting structured information from unstructured text across various domains. A novel approach has been developed to address the variability in domain-specific annotations through the integration of a unified label schema, significantly enhancing cross-domain NER performance. The study involved comprehensive modifications to the Mistral Large model, including adjustments to its architecture, output layer, and loss function, to incorporate the aligned label schema effectively. The methodology encompassed rigorous data collection, preprocessing, and evaluation processes, ensuring robust model training and validation. Evaluation metrics such as precision, recall, F1-score, and accuracy demonstrated substantial improvements, validating the efficacy of the label alignment algorithm. The research highlights the model's ability to generalize entity recognition capabilities across diverse domains, making it adaptable to various linguistic and contextual details. The implications extend to numerous applications reliant on accurate entity recognition, including information retrieval, question answering, and knowledge base population, demonstrating the broader impact of the findings. Through these significant advancements, the study contributes to the development of more intelligent and adaptive NER systems capable of handling the complexities of diverse and evolving textual environments.