In today's world, the rapid evolution of customer expectations has made real-time personalization a cornerstone of effective marketing strategy. This study develops a real-time customer segmentation framework that enables dynamic and personalized marketing campaigns using Salesforce Data Cloud. Salesforce Data Cloud provides the backbone for collecting, processing, and segmenting customer data with powerful data integration and analytics capabilities. It has provided an advanced, machine learning-based framework that dynamically segments customers into classes based on real-time behavioral, transactional, and demographic data. The work also critically reviews traditional segmentation methods, for which real-time demand is increasing to meet analytics for personalized marketing. This approach will collect data from the Salesforce Customer Data Platform, pre-process data to guarantee high quality and implement real-time pipelines to handle streaming data. It integrates Einstein Analytics for visualization and Tableau to provide actionable insights on personalized marketing campaigns. Indeed, the results from this framework provide clear evidence of better segmentation and responsiveness and, therefore, more effective campaigns. These strategies brought about higher response rates and customer satisfaction than the traditional approaches. Moreover, scalability and adaptability features fit this framework perfectly into various industries with different data sets.