India carries one of the heaviest vector-borne disease burdens in the world. Dengue, malaria, chikungunya, japanese encephalitis, and kala-azar together kill thousands of Indians every year and push millions more into catastrophic health expenditure. The country's surveillance infrastructure, while improving steadily, still relies on passive case detection, delayed reporting, and manually aggregated data that reaches decision-makers too late to prevent outbreaks from spreading. Over the past decade, artificial intelligence has begun entering this space through disease forecasting models, satellite-driven risk mapping, social media-based early warning tools, and AI-assisted diagnostics. This paper examines these developments specifically in the Indian context. It surveys the technical tools being deployed, their performance as reported in Indian studies, the regulatory and ethical questions they raise under Indian law, and the institutional obstacles that explain why AI in Indian public health has so far produced more pilot projects than operational systems. The argument advanced here is that AI can genuinely improve India's vector-borne disease response, but only if it is built on better data infrastructure, governed under a coherent regulatory framework, and deployed with the realities of India's district health system in mind rather than the requirements of a research paper.