Hello Mohit,
In our course we have only seen the application of Markov Chains in the Text Generation part. But Markov Chains in itself is a huge field of immense applications on the internet, healthcare and finance. In our case the data we fed to the Markov Chain model was character by character text but that’s not only how it can work.
The basic building block of a Markov Chain model is probability and according to the data we pass we can basically get predictions in any form. You won’t believe on what scale Google uses Markov Chains, it uses this algorithm in their Page Rank system and to track the activity of a user working on their browsers. Predicting stock market prices in finance, gene analysis in healthcare, there are immense applications.
Even the mobile keyboard you are using right now uses Markov Chains to give you word recommendations 
But at the same time, there are certain areas (like NLP and text generation which we saw) where they don’t great results due to a requirement of huge data. In many of these applications I mentioned above LSTMs and RNNs is a very viable alternative and you will be studying that in the coming videos. I myself have implemented this test generation thing using LSTMs and the results were much better 
I hope this clears your doubt 