Few-shot 3D scene reconstruction remains a challenge due to the limited number of viewpoints available for rendering high-quality images. The proposed framework, SFDNeRF, addresses this by integrating semantic feature-driven constraints and hybrid regularization techniques to enhance Neural Radiance Fields (NeRF). By leveraging an improved CLIP model, Alpha-CLIP, for semantic feature extraction and applying a novel combination of frequency and geometric regularization, SFDNeRF significantly reduces the number of required input images to as few as 3 to 8 while still achieving state-of-the-art performance in scene reconstruction quality. Our method not only excels in synthesizing photorealistic and semantically coherent views but also demonstrates robustness against data scarcity. Extensive evaluations on the LLFF and NeRF Blender datasets show that SFDNeRF outperforms existing few-shot NeRF methods, establishing a new benchmark for few-shot 3D scene synthesis. Our ablation studies and qualitative analyses further attest to the individual and collective strengths of the framework’s components, advocating for its efficacy. SFDNeRF’s advancements pave the way for future research in dynamic scene reconstruction and video synthesis with limited data.