This paper investigates the integration of machine learning and Internet of Things (IoT) technologies inside a cloud environment in retail stores. Although IoT, ML, and big data have been studied individually, their combined usefulness and efficiency research is still lacking. To fill this gap, our research suggests a strategy that uses ML-machine learning algorithms and IoT devices to improve consumer experiences and streamline operations in retail settings. This paper collects data through Internet of Things (IoT) devices with sophisticated sensing features, such as fuzzy logic, to track and examine consumer interactions, movements, and behaviours in retail settings. Hence, Product movements can be tracked via RFID-tagged product tracking, providing information about consumer preferences and the popularity of particular products. The cloud securely stores and processes the data produced by these Internet of Things devices, using big data techniques to glean insights from enormous datasets. The most frequently used ML technique is association rule mining, which extracts significant patterns and correlations from the collected data. Retailers can use this to give personalised suggestions and enhance product placement and assortment by gaining better insights into customer preferences, purchasing patterns, and product affinities. Using case studies and real-world examples, we illustrate the effectiveness and advantages of combining IoT and machine learning technology in retail environments. This research advances the field of retail analytics by offering a thorough framework for utilising emerging technologies to promote corporate growth, improve operational efficiency, and provide better consumer experiences.