Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with Grey Relational Analysis (GRA) integrated with Salp Swarm Algorithm (SSA) for the automated Human-Machine Interaction. The proposed architecture is stated as a Hidden Markov model Grey Relational Salp Swarm (HMM_ GRSS). The proposed HMM_GRSS model estimates the feature vector of the variables in the virtual reality platform and compute the feature spaces. The HMM_GRSS architecture aims to estimate the feature vector of variables within the VR platform and compute the feature spaces. Hidden Markov Models are used to model the temporal behavior and dynamics of the system, allowing for predictions and understanding of the interactions. Grey Relational Analysis is employed to evaluate the relationship and relevance between variables, aiding in feature selection and optimization. The SSA helps optimize the feature spaces by simulating the collective behavior of salp swarms, improving the efficiency and effectiveness of the HMI system. The proposed HMM_GRSS architecture aims to enhance the automated HMI process in a VR platform, allowing for improved interaction and communication between humans and machines. Simulation analysis provides a significant outcome for the proposed HMM_GRSS model for the estimation Human-Machine Interaction
