This research presents a cloud-integrated machine learning (ML) system designed to enhance e-commerce operational efficiency. Leveraging Microsoft Azure’s data services (Azure Data Factory, Databricks, ADLS Gen1 and Gen2, and Power BI), along with a Streamlit user interface, the system processes large-scale transactional data for real-time analytics and decision-making. Four specialized ML models address key challenges: logistics clustering optimizes shipping routes; sales forecasting improves inventory management; fraud detection strengthens security; and order cancellation prediction enhances customer retention. The automated data pipeline ensures efficient ingestion, transformation, and storage, minimizing latency and maximizing data accessibility. The interactive Streamlit interface allows users to select and deploy models, while Power BI dashboards provide dynamic visualizations. This integrated approach demonstrates the potential of cloud computing and ML to improve logistics, enhance fraud prevention, and optimize revenue forecasting. While offering scalability, the system necessitates robust security measures to address data privacy concerns. The reliance on historical data also necessitates continuous model monitoring and retraining to mitigate potential biases. This research contributes a practical framework for e-commerce businesses seeking to leverage data-driven insights for improved performance.