<p>Artificial Intelligence (AI) rapidly transforming healthcare across the globe, but the systematic embedding of AI in Indian healthcare is under investigated. This article analyses the potential effects of AI-based descriptive, predictive and prescriptive analytics on healthcare effectiveness and operational efficiencies in India. A mixed-methods approach was used that included; an in-depth review of the literature and a closed-question survey among 300 healthcare professionals, policy makers, and data scientists. The study was based on multiple regression analysis and examined three hypotheses about the effects of AI. Descriptive analytics demonstrated the strongest explanatory power (<i>R</i>² = 0.5858; Adj. <i>R</i>² = 0.5669), with disease patterns (β = 0.575, <i>p</i> &lt; 0.001) and length of stay (β = 0.601, <i>p</i> &lt; 0.001) emerging as key drivers. Model fit was also good for predictive analytics (<i>R</i>² = 0.5662) that patient demographics (β = 0.501, <i>p</i> &lt; 0.01 but health disparities negatively impacted it β = –0.605, <i>p</i> &lt; 0.001) significantly predicted better outcomes. Prescriptive analytics accounted for 45.6% of the variance (<i>R</i>² = 0.4559) with resource use (β = 0.438, <i>p</i> &lt; 0.01) and length of stay (β = 0.539, <i>p</i> &lt; 0.001) as significant positive factors and disease profiles and healthcare expenditure constraining model performance. In short, AI solutions enhance decisions and system performance but are very conditional on data quality, resource parity and costing aspects. The article proposes a theoretical framework of AI analytics having direct and indirect effects on organizational performance. A robust policy framework, fair data access and strategic investments is required to unleash AI’s potential in Indian healthcare.</p>