The analysis and investigation of the data obtained from Direct Numerical (DNS) simulation of droplet dynamics in cloud turbulence is a complex and time-consuming task when performaed on traditional computers. The DNS data generally have, a high spatial resolution ≈1mm and require considerable space to store. It is tedious to find specific features of this data, such as locating high and low vortex areas in cloud turbulence using machine learning algorithms. In this research, we employ quantum computing to examine and analyze cloud droplet dynamics data and present a quantum supervised machine learning algorithm, namely, a support vector machine (SVM) to segregate low and high vortex regions and investigate the droplet characteristics in those regions. The result show that use of quantum computers can accelerate the entire process, and quantum mechanics tools, such as quantum kernels and quantum circuits can better manage the complex nature of data than traditional methods.