Customer segmentation is a crucial process in marketing, involving separating a customer database into distinct groups of customers who have similar characteristics or needs. This segmentation enables businesses to develop targeted marketing strategies that are more effective in reaching and engaging their customers. One popular method of customer segmentation is to use machine learning clustering algorithms, which classify customers based on their similarities in demographics, behavior, and purchase history. To perform customer segmentation, businesses collect and analyze large amounts of data from various sources, such as consumer surveys, purchase histories, and social media activity. This data is then analyzed using statistical models and algorithms to identify patterns and trends that can be used to segment the client base. Machine learning clustering algorithms are particularly useful for customer segmentation because they can analyze large datasets quickly and accurately, allowing businesses to segment their customers more efficiently. By using machine learning clustering algorithms, businesses can quickly and accurately analyze customer data to identify distinct customer segments, enabling them to develop marketing messages that resonate with their target audience. The output of the proposed model shows the most popular product categories based on the number of reviews submitted for each category. This information will be helpful in determining which products are popular among customers and which ones need more promotion.