E-commerce platforms have brought about a significant transformation in the retail industry during the digital age, particularly impacting the women’s apparel sector. Businesses are prioritizing the enhancement of customer experience to remain competitive and foster customer loyalty in the evolving landscape of e-commerce. Real-time sentiment analysis using AI and NLP has unlocked the potential for hyper-personalization. Wang et al.’s work highlighted the importance of understanding customer sentiments for tailoring product recommendations, marketing messages, and service interactions. The data were gathered using the Web Scraper.io application. Following data preparation, the dataset was split into 75:25 training and testing sets. The training set was utilized to train the machine learning models, while the testing set was used to evaluate their F1 score, precision, recall, and accuracy. In summary, AI and NLP for customer sentiment analysis are reshaping the landscape of Women’s Apparel E-commerce. Two methods logistic regression and random forest classifier were selected, logistic regression method was a better as compared to random forest classifier.