Age estimate using facial images is a fascinating and challenging issue. The characteristics from the face images are utilized to assess people's age, gender, ethnic origin, and emotion. Among this group of characteristics, age estimates can be beneficial in numerous possible real-time applications. Deep learning has recently achieved great success. Hence, we are using the Generative Adversarial Network (GAN) based method for automatic aging of faces. GAN produces images by altering facial attributes, and we create them to preserve the original person's identity in any age version. The deep generative networks have exhibited a remarkable capability in image generation. To the end, we introduced an approach for Identity-Preserving and GAN's Latent vector optimization. The evaluation of the objective of the proposed method demonstrates the following results proposed framework produced more realistic by comparing the state-of-art and ground truth. It can also be used for cross-age verification. We will be using the Dataset of MORPH and CACD to train our GAN model as it requires much data to learn. Moreover, an adversarial learning technique is presented to train a generator and parallel discriminators simultaneously, resulting in smooth continuous face aging sequences. © 2021 IEEE.