A combination of quantum mechanics principles and metaheuristic algorithm frameworks has given birth to quantum-inspired soft computing techniques. These methods augment population diversity, which is vital for effective global exploration, through the probabilistic nature of quantum bits. This paper also describes recent developments in quantum-inspired soft computing techniques, highlighting the merits of linking optimization techniques with the fundamentals of quantum mechanics in a wide variety of practical and industrial domains. Additionally, we discuss these algorithms’ enhancements and modifications and recognize the problems within this area. We collated algorithms presented from 2017 to 2023 and grouped them according to their motivating basis. Quantum-Inspired Soft Computing mainly uses Genetic and Evolutionary Algorithms as its primary methods. Following these, Swarm-Based Techniques are the next most widely applied approaches. Quantum-Inspired Neural Networks also play a role in this field, though they are used less frequently than the first two methods. Some areas where these methods have been applied include image processing and network optimization, as well as interdisciplinary areas like aviation management and civil engineering. The positive outcomes obtained from quantum-inspired soft computing imply that traditional algorithms can be enriched in the future by using the concepts of quantum to address optimization challenges across various domains.