In today’s modern life and technologies, businesses face many challenges, including understanding customer behavior across multiple retail environments. For instance, customers’ behavior might differ in wholesale, online retail, and mall shopping. Each of these environments provides unique insights into different customer purchasing patterns and behaviors. Those behaviors affect the marketing strategies and, at the same time, enhance customer behaviors. Therefore, this paper utilizes Artificial Intelligence techniques and machine learning technologies to produce a comprehensive tool to integrate and analyze customer data from different datasets. The selected datasets represent Wholesale Customers, Online Retail, and Mall Customers. The proposed model is designed to develop a unified customer segmentation model, predict customer lifetime value (CLV), identify cross-selling opportunities, detect anomalies, and forecast customer churn. Different algorithms are to be applied, such as clustering, regression, association rule mining, anomaly detection, and classification algorithms—consequently, a holistic view of customer behavior based on different retail channels. The output of this paper offers insights for business to improve their customer satisfaction and their business plans and marketing strategies. Therefore, this research contributes to the importance of the integration of multiple channel data and AI success in doing so.