Customer segmentation and prediction, driven by machine learning algorithms, plays a vital role in today’s fast-paced and data-driven market. This paper focuses on applying machine learning techniques, specifically the k-means clustering algorithm and Medium Gaussian Support Vector Machine(SVM) regression learning, for customer segmentation and prediction. The process starts with data col-lection and cleaning to ensure the reliability of the data clarity gained. The k-means clustering algorithm plays a central role in identifying patterns within customer data, revealing natural groupings that may not be apparent through traditional analysis methods. Using machine learning algorithms, with a focus on k-means clustering, the segmentation process is refined, allowing for the discovery of complex patterns in customer behavior. This, in turn, enables the creation of personalized interactions through predefined customer segments. Furthermore, the segmented customer information is analyzed using different regression techniques to predict segmented customers better. Experimental results show that the proposed approach achieves the lowest mean average error of 15.723 with a model size of 12KB. The advantages of this approach are substantial, including the development of tailored marketing strategies, personalized recommendations, and the optimization of resource allocation. This understanding allows companies to create customized approaches for each group, leading to happier customers, more people recommending their services or products, and a better overall image for the brand.