The global aging population poses unprecedented challenges to healthcare systems, with 1 in 6 people worldwide expected to be over 65 by 2050 (UN, 2024). Geriatric health management—focused on chronic disease (e.g., osteoporosis, chronic obstructive pulmonary disease [COPD]) and age-related conditions (e.g., mild cognitive impairment [MCI])—requires personalized, continuous care that conventional models struggle to provide. This study explores how artificial intelligence (AI) technologies, including wearable sensor analytics, computer vision, and natural language processing (NLP), address these gaps. We analyze 18 real-world AI implementations (2022–2025) across 12 countries, showing AI-driven chronic disease monitoring reduces emergency hospital visits by 32–40%, cognitive impairment screening cuts diagnostic time by 50%, and remote care platforms improve patient satisfaction by 45%. Ethical considerations, such as digital literacy disparities and data privacy for elderly populations, are addressed through a tailored governance framework. Findings highlight AI’s potential to enhance quality of life and reduce healthcare costs for aging populations.