Triple Generative Adversarial Networks (Triple-GANs) are an advanced extension of generative adversarial networks (GANs) designed to jointly model data generation, class-conditional generation, and classification tasks within a unified framework. Unlike conventional GANs that focus solely on data synthesis, Triple-GAN introduces three interconnected components: a generator that produces realistic samples, a classifier that predicts labels, and a discriminator that distinguishes between real and generated data while assessing label consistency. This architecture enables semi-supervised learning, improves classification accuracy with limited labeled data, and enhances generative quality. Research in Triple-GANs explores variants for image synthesis, domain adaptation, anomaly detection, and text-to-image generation, often integrating convolutional and attention-based networks to handle complex, high-dimensional data. Applications include semi-supervised image classification, data augmentation, medical image analysis, and multi-modal learning. Current studies also focus on stability improvements, training efficiency, and extension to multi-class and multi-modal scenarios, establishing Triple-GANs as a powerful framework for simultaneous generation and classification in deep learning systems.