Measles is one of the most contagious viral infections and continues to persist worldwide, challenging public health systems in spite of extensive vaccination efforts. In this work, we develop a stochastic \(SV_1V_2EIH\) model to describe the transmission dynamics of measles. It incorporates both nonlinear white noise and colored noise driven by an irreducible continuous-time Markov chain with discrete state variables representing media awareness and environmental perturbations. The model is calibrated using measles case data from India spanning the years 2014 to 2024, allowing for the estimation of key epidemiological parameters. The proposed stochastic model is proven to admit a unique global positive solution for all time. Sufficient criteria involving \(R_0^s\) establish the existence and uniqueness of an ergodic stationary distribution, reflecting long-term disease persistence. In contrast, conditions for disease extinction are obtained under the threshold \(R_0^e\) . Finally, numerical simulations validate the theoretical findings and demonstrate consistency between the model and observed dynamics. The results indicate that stochastic effects and media awareness can significantly influence measles transmission outcomes, either sustaining endemicity or promoting elimination.
