Formal fuzzy concept analysis is an effective data analysis and mining technique in the real world. However, deriving formal fuzzy concepts is an NP problem that demands substantial time and storage resources. With the continuous exponential growth of real-world data, there is a need to regularly update the extracted list of fuzzy concepts. This research paper presents a novel and efficient algorithm to update the extracted fuzzy concepts when inserting new data objects. The proposed algorithm eliminates the need for regenerating fuzzy formal concepts by reprocessing the entire dataset. Instead, it processes only the changed part and merges it with the old list of fuzzy concepts. We have evaluated the proposed approach over various datasets of different types: quantitative, categorical, and synthesized fuzzy data. The experimental results demonstrate that the proposed algorithm outperforms the traditional approach of fuzzy concept extraction by updating only the extracted fuzzy concepts rather than recreating them from scratch, especially in the case of massive data sets.