Ultra-low-field (ULF) Magnetic Resonance Imaging (MRI) improves accessibility and affordability but suffers from lower image quality compared to high-field MRI. This study proposes a novel enhancement framework that integrates Implicit Neural Representations (INRs) with Neural Style Transfer (NST) to improve ULF MRI quality by transferring high-resolution structural details from 7T MRI. Unlike conventional methods, our approach does not require paired datasets or extensive pre-training, leveraging INR’s continuous representation and NST’s perceptual refinement to enhance contrast, sharpness, and noise suppression. Quantitative evaluations on T1-weighted ULF MRI demonstrate significant improvements in perceptual quality (PIQE), contrast-to-noise ratio (CNR), and structural consistency (MLC/MSLC), outperforming state-of-the-art methods. These findings underscore the potential of INR-driven learning for advancing MRI reconstruction, enabling higher-quality imaging in resource-limited settings. Our method is fully unsupervised and operates in an unpaired setting, requiring no voxel-wise correspondence or labeled training data. The implementation of our proposed method and model hyperparameters is publicly available at https://github.com/khtohidulislam/ULF-MRI-Enhance .