The evolving landscape of digital technologies necessitates robust solutions for synthetic data generation and cybersecurity. Recent breakthroughs in quantum computing have created new avenues for the advancement of quantum generative adversarial networks (QGAN), which combine quantum computing with the generative capabilities of classical GANs. Meanwhile, the field of synthetic data generation has seen significant progress, particularly with the use of classical GANs to create realistic synthetic datasets for various applications . This survey synthesizes recent advances in QGANs, classical GANs, and other deep generative models, focusing on their applications in synthetic data generation and intrusion detection systems (IDS). By integrating insights from multiple studies, we present a thorough comparison of approaches, assessment methods, performance metrics, and possible future directions. Our comparative study highlights the unique contributions and potential synergies between quantum and classical approaches to generative modeling, revealing the strengths and drawbacks of each approach. Furthermore, the integration of quantum key distribution (QKD) and quantum neural networks (QNN) for optimization and enhanced security is proposed as a forward-looking approach. These insights offer valuable guidance for future research directions and practical implementations in the domains of synthetic data generation and cybersecurity.