The food sector is facing an obstacle worldwide in producing safe, high-quality, and shelf-stable food. Chemical additives are therefore frequently utilized to make sure that food retains all of its favorable sensory qualities throughout its shelf life, ensuring consumer preference at the moment of purchase which can lead to adverse health issues in long run. A range of natural products have gained attention as food ingredients and bioactive substances added to foods and health products as a substitute for chemical additives. Flavonoids are the major group of polyphenols present in the natural products. The market for flavonoids is anticipated to be driven by the worldwide shift towards healthy lifestyles and increased awareness of the advantages of natural products. Modern flavonoid extraction methods fulfil the needs of the pharmaceutical and nutraceutical industries by improving efficiency, using less energy and solvent and improving selectivity. The high demand for flavonoid consumption prompts the development of novel sample pretreatments and analytical techniques for their determination in various sources and in active product development. Optimization of the addition of natural extracts in foods and their characterization opens door for engaging artificial intelligence in anticipating the datasets obtained. Large datasets can be analyzed by AI algorithms, especially machine learning, to find patterns and pinpoint important variables affecting extraction, which boosts yields and improves efficiency. This read focusses on flavonoids, their extraction techniques, lime lighting on green extractions and compound analysis coupled with machine learning models.