This study explores the application of generative adversarial networks (GANs) for data generation in local contact search. It examines GANs as an innovative approach to producing reliable datasets for contact searches. The objective is to identify an optimal GAN model that achieves high accuracy in data generation. The process begins with the creation of an initial dataset of varying sample sizes, followed by the development of a GAN model to generate additional synthetic data. The quality of the generated data is evaluated using several error metrics, including mean squared error and mean absolute error, along with kernel density plots. Additionally, the study investigates the effects of different kernels on the kernel density plots, comparing the kernel density estimations of the synthetic data against the actual data generated using the Newton–Raphson method.