One of the biggest challenges faced by the health insurance industry in India is determination of a comprehensive, fair, and adequate price for insuring different sets of benefits. In this paper, we investigate how relatively new techniques such as tree-based machine learning (ML) models perform compared to the classical actuarial pricing approach of generalized linear models (GLMs), along with capturing the current adoption level of such techniques across India through a survey. The paper's main sections include a case study on ML techniques for pricing and a survey showing the adoption level of ML across the industry.