The integration of generative deep learning techniques in mental healthcare is at the center of its improvement aiming towards better diagnosis, treatment customization, and even prediction of the conditions. This chapter focuses on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) and associated generative deep learning models about mental health issues. It does so by modeling how these systems process various combinations of patient behaviors, language, communication, and physical biometrics to decipher and make predictions about patterns of behavior and trends that are mostly out of reach of classical analysis. While the purpose or use of generative models has mainly been training enhancement, these models have been advantageous in that they have also prompted the evolution of treatment tailoring by constructing models that represent within a patient-specific active mental state. One of the examples mentioned is the use of artificial intelligence systems for mental health assessment, whereby deep learning is employed to elicit possible abnormalities by looking at how patients talk and express their emotions through their faces. On the other hand, this technology can be used in developing responsive virtual mental health assistants that adjust their interaction strategies to the user in real-time, providing personalized attention. This technology, however, has its fair share of limitations such as data privacy, concerns over unfair biases in the training data, and the use of artificial intelligence in healthcare. Each of these issues is addressed by the multidisciplinary, technical, clinical, and regulatory approaches necessary to fully exploit all these tools. By incorporating generative deep learning techniques in the practices of mental health, this chapter anticipates a day when mental healthcare systems will be accurate, made ahead of time, and tailored, prognostic services.