Diabetes Mellitus remains one of the fastest-growing global metabolic disorders, leading to severe multi-organ long-term failure if undetected. Early diagnostic classification serves as a crucial mechanism for patient risk mitigation. This paper presents 'CM Health Care', a comprehensive, production-grade cloud- integrated AI framework that closes the loop between algorithmic predictive accuracy and actual clinical workflow deployment. Utilizing the Pima Indian Diabetes Dataset, we implement a highly optimized Logistic Regression engine utilizing automated feature imputation via custom-calculated localized central tendencies. The core statistical engine achieves a benchmark classification accuracy of 75.97% with highly stable generalized gradients. To bridge the gap between machine intelligence and practical medical utilization, we design a state-of-the-art

dual-portal Streamlit ecosystem supporting asynchronous secure Multi-Factor Authentication via automated SMTP One-Time Password tokens. The platform accommodates customized clinical dashboards for physicians, full patient risk profiling pipelines, and dynamically generated PDF prognostic health reports. This study details the end-to-end framework from statistical preprocessing through to production-ready database management, demonstrating a highly reproducible template for intelligent modern health informatics networks.