In this paper, the authors perform a state-of-the-art literature review to classify and interpret the ongoing and emerging issues associated with the Hidden Markov Model (HMM) in the last decade. HMM is a commonly used method in many scientific areas. It is a temporal probabilistic model in which the state of the process is described by a single discrete random variable. The theory of HMMs was developed in the late 1960s. Now, it is especially known for its application in temporal pattern recognition, i.e. speech, handwriting, and bioinformatics.