Can HMM be used as a binary classifier? I have some time-series data, which I need to use to predict a binary label for a given time-stamp. I was initially doing it using a Logistic Regression Model but I later figured that a time-series based problem could be better solved using Hidden Markov Models. I might also save on time since this prediction is to be made for ~200,000 time-stamps.

I have been able to obtain the next n hidden states using the hmmlearn implementation, but what I really need are 0/1 labels.

How can I (if possible) use HMM as a binary classifier?
I have no experience in hmmlearn. 

But if you want to receive the labels for the underlying states of the process you have to do 'decoding' by using the 'Viterbi' algorithm. It calculates the most probable state sequence for your data. So if you only have two hidden states it calculates a 0/1 label for each of your time-stamps.

If instead you're trying to discern between two different processes and recognize them, you have to train two different HMMs for the two processes and calculate the foward probabilty up to time t. Then you can choose the process with the higher probability.