We present MultiNeRF, a novel 3D watermarking method that enables embedding multiple uniquely keyed watermarks within images rendered by a single Neural Radiance Field (NeRF) model while maintaining high visual quality. Our approach extends the TensoRF NeRF model by incorporating a dedicated watermark grid alongside the existing geometry and appearance grids. This ensures higher watermark capacity without entangling watermark signals with scene content. We propose a FiLM-based conditional modulation mechanism that dynamically activates watermarks based on input identifiers, allowing multiple independent watermarks to be embedded and extracted without requiring model retraining. We validate MultiNeRF on the NeRF-Synthetic and LLFF datasets, demonstrating statistically significant improvements in robust capacity without compromising on rendering quality. By generalizing single-watermark NeRF methods into a flexible multi-watermarking framework, MultiNeRF provides a scalable solution for securing ownership and attribution in 3D content.