Companies with a strong customer emphasis are the primary users of data mining nowadays. Inconsistencies and redundancies are not immediately applicable for starting a data mining process since data is likely to be faulty. In data analysis, missing values are unavoidable, and cause serious problems. Faulty knowledge is retrieved and incorrect inferences are drawn when missing values are handled and replaced incorrectly, thereby leading to high computational time with high data and large iterations. Due to heterogeneous datasets, there is a lack of exact mapping technique, and while visualizing the extracted data the mild handling of data leads to inaccurate data extraction, which lowers all the efficient processes contributing towards the accuracy. Hence in this paper, to overcome the issues a novel Big Data Food Mapping framework is proposed which incorporates association rule mining, followed by a fuzzy clustering-based binning procedure followed by a score-based normalization. Map Reducing, including initial mapping based on genetic operators and entropy generation, was also performed. The reduction is done using fuzzy logic and a Graph Neural Network. After that, the data is mined using Concept analysis and Long Short -Term Memory (LSTM). The outcomes were more accurate and displayed using Tdistributed stochastic neighbor embedding (t-SNE) based dual clustering.