Chronic diseases such as diabetes, cardiovascular conditions, and metabolic syndromes have emerged as the leading causes of mortality and healthcare expenditure globally. Traditional healthcare systems, primarily reactive and fragmented, often fail to detect chronic conditions early or manage them effectively. Simultaneously, the pharmaceutical sector faces challenges in peptide-based therapeutic manufacturing, including variability, inefficiency, and high production costs. This paper proposes a comprehensive, multi-layered AI framework that addresses these dual challenges by integrating Electronic Health Records (EHRs), genomic data, real-time patient monitoring through Internet of Medical Things (IoMT) devices, and pharmaceutical bioreactor telemetry. The architecture leverages cloud-native data integration, predictive analytics through machine learning and deep learning models, conversational AI agents for patient engagement, and digital twins combined with reinforcement learning (RL) to optimize peptide drug manufacturing. Quantitative results demonstrate significant improvements: early disease detection models achieved a ROC-AUC of 0.92 and an F1-score of 0.87, while pharmaceutical optimization reduced cycle times by 40% and improved quality control rates by 17%. The framework is designed with ethical AI principles, including bias mitigation, human-in- the-loop validation, federated learning for privacy preservation, and explainability through SHAP and LIME methods. This research offers a scalable, ethical blueprint for transforming both chronic disease management and pharmaceutical manufacturing, ensuring broader access, greater efficiency, and enhanced patient outcomes.