Hybrid generative–discriminative models have become a strong model in cybersecurity because they combine strengths that help solve problems that have been around for a long time, such as detection accuracy, data imbalance, adversarial robustness, and zero-day generalization. This review combines new work that combines generative models like variational autoencoders, generative adversarial networks, diffusion models, and probabilistic graphical models with discriminative models like convolutional networks, recurrent and transformer-based models, graph neural networks, and traditional machine-learning classifiers. The research offers a systematic classification of hybrid methodologies, emphasizing joint, sequential, parallel, and adversarially focused frameworks. It also looks at how well they work in important areas of cybersecurity, such as finding intrusions, analyzing malware, spotting fraud and anomalies, classifying phishing and spam, and defending against attacks. Key performance insights show that hybrid models are always better than standalone classifiers at recognizing minority classes, representing quality, and being able to handle new threats. Even though they hold a lot of promise, there are still big problems with scalability, explainability, data privacy, and defense against adaptive attackers. This review gives a complete picture of the current limitations and future research directions for the next generation of intelligent, resilient, and trustworthy cybersecurity systems. These include multimodal foundation models, continual learning, privacy-preserving hybrid pipelines, and standardized benchmarks.