It is important that multidisciplinary community of scientists first investigate all the myriad potential directions that quantum machine learning might go to discover what it has to offer. Among these avenues, specified algorithms such as Variational Quantum Algorithms (VQAs) have been applied in different industries for simulation, optimization, and prediction purposes. The focus of this paper is to determine the principles associated with the use of VQAs in chemistry, machine leaning, and optimization, amongst many other developed algorithms. The key results indicated that VQAs can successfully be applied in the above-mentioned fields, with the goal to (1) find ground and excited energy states of different molecules in chemistry; (2) maximizing or minimizing an objective function under specified constraints in optimization; (3) developing trainable quantum models for accurate predictions of unknown and unseen datasets. After statistical analysis, it was discovered that the proportion of publications of VQAs for optimization, machine learning, and chemical purposes have been considerably high over the past 6 years. This is justified by the sparking attention that parametrized circuits have offered over the recent years. After comparing different size effects, it was discovered that the fields of application of VQAs are interconnected, since they presented a small effect size, with Cohen’s d value being less than 0.2. From these observations, it can be concluded that the lessons developed from one application can serve as direction for others. The metallurgical industry as a field of chemistry can benefit from the methods developed in VQAs for optimization, simulation, and development of novel materials.