The proliferation of social media platforms has provided a vast source of real-time user-generated content, enabling innovative approaches to public health monitoring. This study explores AI-augmented social health analytics to extract mental and physical well-being insights from social media and online networks. Leveraging natural language processing (NLP), machine learning (ML), and deep learning techniques, we analyze sentiment trends, stress indicators, and health-related discussions to quantify mental health conditions such as anxiety and depression, as well as physical health trends like pandemic outbreaks and lifestyle diseases. Findings indicate that social media sentiment analysis correlates with mental health trends, with an accuracy of 87% in detecting stress levels. AI models trained on diverse datasets achieve a precision of 82% in identifying depressive symptoms based on linguistic markers and engagement patterns. Furthermore, deep learning-driven physical health analytics successfully predict disease spread patterns with a mean error reduction of 23% compared to traditional epidemiological models. The results demonstrate that AI-driven social media analytics can provide real-time, scalable, and cost-effective insights into public health, aiding in early interventions and policy-making. Future research should focus on enhancing multi-modal analysis, bias mitigation, and ethical considerations to improve the robustness and fairness of AI-augmented social health analytics.