<p>In the realms of next-generation internet, the metaverse aims to offer an entirely immersive and self-sustaining virtual environment for diverse activities and interactions. Recent advancements in blockchain technology, extended reality (XR) and artificial intelligence (AI) have paved the way for the metaverse to become an imminent reality. However, a longstanding challenge has been synthesizing novel views of intricate scenes within the simulated environment from a constrained set of input images. The advent of neural radiance field (NeRF) has significantly eased the process of rendering photorealistic views of scenes with intricate geometry, achieved by optimizing a continuous volumetric scene function. This study offers a comprehensive review and analysis of pioneering NeRF advancements in the context of the metaverse. Additionally, we introduce a novel taxonomy for the categorization of NeRF mechanisms, primarily based on their application areas and conduct an in-depth examination of architectural modifications in NeRF models. We also elucidate performance protocols, including commonly employed benchmark datasets and evaluation metrics for assessing these algorithms. Despite the existing contributions in the literature on view synthesis, numerous open research problems still persist, warranting the attention of researchers in this active field of study.</p>