The coefficients of the stochastic differential equations with Markovian switching (SDEwMS) additionally depend on a Markov chain and there is no notion of differentiating such functions with respect to the Markov chain. In particular, this implies that the Itô-Taylor expansion for SDEwMS is not a straightforward extension of the Itô-Taylor expansion for stochastic differential equations (SDEs). Further, higher-order numerical schemes for SDEwMS are not available in the literature, perhaps because of the absence of the Itô-Taylor expansion. In this article, first, we overcome these challenges and derive the Itô-Taylor expansion for SDEwMS, under some suitable regularity assumptions on the coefficients, by developing new techniques. Secondly, we demonstrate an application of our first result on the Itô-Taylor expansion in the numerical approximations of SDEwMS. We derive an explicit scheme for SDEwMS using the Itô-Taylor expansion and show that the strong rate of convergence of our scheme is equal to $γ\in\{n/2:n\in\mathbb{N}\}$ under some suitable Lipschitz-type conditions on the coefficients and their derivatives. It is worth mentioning that designing and analysis of the Itô-Taylor expansion and the $γ\in\{n/2:n\in\mathbb{N}\}$-order scheme for SDEwMS become much more complex and involved due to the entangling of continuous dynamics and discrete events. Finally, our results coincide with the corresponding results on SDEs when the state of the Markov chain is a singleton set.