Advancements in Artificial Intelligence (AI) have shown remarkable promise in early disease detection, exemplified by intelligent tools such as MIRAI, which uses deep learning on routine mammograms to predict breast cancer risk up to 5 years in advance. Simultaneously, ancient healthcare systems like Ayurveda have emphasized cyclical health patterns (Doshas), lunar influence, and body–mind rhythms. This chapter reviews how emerging studies are exploring the convergence of these domains to enable rhythmic intelligence, a descriptive concept rather than a formal framework, where AI augments cyclical well-being without proposing new methodologies. Initial investigations show how tools like Prakriti Analyzers and Nadi Tarangini are beginning to incorporate machine learning to detect Dosha-related imbalances, potentially enhancing diagnostic accuracy. Concurrently, wearable and edge-computing platforms are generating real-time insights into circadian and lunar-linked phenomena ranging from menopausal hormone shifts to seizure risks, that may correlate with moon phases or traditional Panchakarma cycles. By collecting biosensor data aligned with Ayurvedic timekeeping, these systems allow us to observe once anecdotal patterns, using serial data to model physiological trends. MIRAI’s global performance, validated on over 2 million images from diverse institutions and geographies, demonstrates that AI can reliably detect long-term health risks in a scalable way. While no single model holistically integrates Ayurveda and lunar rhythms yet, ongoing trials suggest substantial potential for culturally attuned, personalized healthcare solutions. This chapter synthesizes evidence from these strands, i.e., AI-powered medical imaging, traditional diagnostics, and cyclic biosensing, and outlines a research trajectory to responsibly unite them. Ethical insights, data privacy, and equitable deployment in underserved communities will be integral to this evolution toward anticipatory, culturally informed health care.