The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed smart healthcare systems, enabling data-driven decision-making, real-time monitoring, and predictive analytics. This chapter presents a comprehensive bibliometric and content analysis of AI applications in smart healthcare from 2015 to 2024, using a dataset of 3549 publications from the Scopus database. A mixed-methods approach was employed, integrating quantitative statistical summaries with qualitative content analysis to identify research trends, prolific contributors, technological focus areas, and publication dynamics. The analysis reveals a dramatic growth in publication output, rising from 7 records in 2015 to 1377 in 2024, reflecting an overall growth rate of 19,614% over the decade. Descriptive statistics indicate a median publication year of 2023, with citation counts ranging from 0 to 4694, highlighting a skewed distribution where a few highly cited works dominate scholarly attention. IEEE Access emerged as the leading journal with 140 publications, while India led national contributions with 1159 records, followed by the United States (405) and China (258). Keyword analysis shows “deep learning” (n = 1222), “machine learning” (n = 592), and “artificial intelligence” (n = 536) as dominant thematic areas, with notable attention to “medical imaging” (n = 405) and “transfer learning” (n = 178). Visualization techniques such as line plots, word clouds, and bar charts were used to illustrate temporal trends and thematic evolution. The study also identifies Kumar A. as the most prolific author with 31 publications, and Chitkara University as the top contributing institution with 38 records. This chapter provides actionable insights for researchers, policymakers, and practitioners by mapping AI’s intellectual landscape and technological progression in healthcare. It highlights the explosive growth and the thematic shifts in the field, pointing to underexplored areas such as ethical AI, explainability, and deployment in low-resource settings. The findings underscore the transformative potential of AI in building resilient, personalized, and efficient smart healthcare systems.