Now, by incorporating Reinforcement Learning (RL), Generative Artificial Intelligence (AI) has now turned into a strong paradigm where the machines themselves can learn independently through interaction with the environment and generate complex, imaginative output. With reinforcement learning, guided by a reward signal that encourages desired outcomes from trial and error, it allows the generative models to explore very big and often unexplored solution spaces. It is in great contrast to machine learning, which, in most cases, deals with model training on labeled datasets. Consequently, it has been highly effective in a wide domain of creation, ranging from art to music and from writing to video game environments-all because of the optimization performed for creativity, innovation, and user-specific preferences. Some reinforcement learning techniques applied in Generative AI are DQN, Policy Gradient, and Actor-Critic, which help to train GANs and VAEs in dynamic and uncertain situations. These models can generate diverse outputs with high quality since reinforcement learning enables them to improve decision-making techniques progressively. Apart from that, it gives an interface with which to improve the AI systems through user input, making the generative tasks personalized. The review in this paper concerns the theoretical core of reinforcement learning in the generative AI area, how the former is combined with the latter among the popular generative models, and practical applications where the generative models based on RL have given results with state-of-the-art performance. It also covers challenges and possible ways forward for reinforcement learning to improve generative capabilities, including incentive engineering, computational complexity, and moral considerations. The combination of Generative AI with Reinforcement Learning can create increasingly creative, interactive autonomous AI systems that will spur innovation in a host of industries.