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The beauty products and cosmetics sector in India has grown rapidly due to shifting customer preferences and a greater focus on personal grooming. With the continued growth in sales of beauty products, it is critical for firms looking to succeed in this cutthroat industry to comprehend the dynamics of customer ratings and how they affect the quality of their products. Firstly, this study identifies major trends, obstacles, and possibilities influencing the nexus between artificial intelligence and the Indian beauty goods market by utilizing the body of current literature and empirical data. Secondary data is used in this research, two Kaggle datasets--Amazon Beauty Product Sales Rating and Amazon Beauty Product Recommendation--are analyzed in this study. We seek to provide insight on the complex relationship between customer ratings and product quality and how these aspects affect the success of beauty goods in the Indian market by contrasting these datasets. The objective of this study is to stimulate advancements in the domain of beauty product sales in India by establishing a connection between theoretical research and operational implementations. In the conclusion, our summary of the literature seeks to guide future initiatives focused at leveraging AI to promote innovation, growth, and sustainability in the ever-changing Indian beauty goods sector. The research's conclusions have ramifications for Indian consumers and producers of cosmetic products. This study has results for Indian consumers as well as cosmetic product manufacturers. Customer feedback may provide manufacturers with strategic insights to improve the quality of their products, and customers can use it to make well-informed decisions that suit their tastes. In the end, this research adds to the larger conversation on the dynamics of consumer happiness and product quality in India's thriving beauty and cosmetics industry. [ABSTRACT FROM AUTHOR]