Neural rendering is an emerging research area at the intersection of computer graphics and deep learning, focusing on generating photo-realistic images or videos from high-level scene representations using neural networks. Early approaches combined classical rendering techniques with learned components to enhance realism, while recent methods leverage convolutional neural networks (CNNs), generative adversarial networks (GANs), and implicit neural representations such as Neural Radiance Fields (NeRF) to model complex geometry, lighting, and material properties. Neural rendering techniques enable tasks such as novel view synthesis, image-based rendering, 3D reconstruction, relighting, and texture transfer with unprecedented fidelity. Applications span virtual and augmented reality, gaming, film production, robotics, and scientific visualization. Current research also explores real-time neural rendering, integration with multi-modal inputs (e.g., depth, RGB, and semantic maps), and generalization to dynamic and large-scale scenes, positioning neural rendering as a transformative paradigm for bridging physical and learned visual representations.