The aim of this paper is to delve into an important aspect of human–computer interaction (HCI) to become familiar with different techniques used in hand gesture recognition. Hand gestures are nonverbal forms of communication in which information, emotions, or messages are conveyed by hand movements and positions. They are a universal mode of communication that can be used to supplement or replace spoken language. The study also comprehensively examines all the algorithms and models, like convolutional neural networks (CNNs), hidden Markov model (HMM), latent Dirichlet allocation (LDA), mean-shift, etc. We assess their strengths and weaknesses according to their hand gesture detection and recognition performance. The paper also explores past trends of this ever-growing technology. We have studied around 30 papers to evaluate different techniques used for hand gesture recognition to date. Hand gesture recognition is so important for review as it is extensively used in many domains like the gaming industry and health care and has many more limitless opportunities.