Generative adversarial networks (GANs) are playing a very significant role in digital forensics at present. Despite the popularity of GAN applications, GAN training is difficult and suffers from a few pathologies like mode collapse, vanishing gradients, and non-convergence that produce misleading outcomes. Providing solutions to these issues, a novel approach called the Generalized Ensemble GAN Model is devised. It is solely based on the combination of a single generator with three distinct CNN-based discriminators based on the voting ensemble technique. This paper offers an extensive comparative study of the ensemble model with existing GAN Variants Cycle GAN (CGAN), Deep Convolution GAN (DCGAN), and Semi-supervised GAN (SGAN). The comparison of the models is executed based on four quantitative parameters. Inception Score (IS), Fréchet Inception Distance (FID), Structural Similarity Index (SSIM), and Total Computational Time. To achieve insights about performance, the diverse behaviors of the models are analyzed using the Indian Actor Images Dataset. It is analyzed that the Ensemble GAN model outperformed the other GAN models, proving the feasibility and effectiveness of the technique. Outperforming DCGAN, SGAN, and CGAN in all of these characteristics, the Ensemble GAN produced a minimal TCT of 1009.4091 s, a low FID of 4.126, a high IS of 2.67607, and an SSIM of 0.0643. This work presents the Generalized Ensemble GAN Model as a reliable option for enhanced synthetic picture fidelity and stable GAN training, with encouraging ramifications for digital forensic applications.