Multimodal medical image fusion is the process of obtaining relevant information from medical images. In order to create a single image that is better suitable for diagnosis, multiple images from various sources are combined in image fusion for medical images. Since many of the structures in medical images are rarely discernible and the merged image typically shows blurring and lags behind the accompanying data, the complexity of medical images is higher. The purpose of this research is to present a novel hybrid approach based on fuzzy sets and Undecimated Discrete Wavelet Transform (UDWT) for improved visual analysis and to lessen blurring of medical images. There is no decimation stage in UDWT. It is a non-orthogonal multiresolution decomposition. In medical images, fuzzy sets are essential for reducing ambiguity. This research paper UDWT after fuzzifying the source photos. The maximum selection criterion is used to combine low-frequency subbands in the UDWT domain, whereas the Modified Spatial Frequency (MSF) technique is utilized to combine high-frequency subbands. The inverse UDWT creates the merged image. Several pairs of photos are used to demonstrate multimodal medical image fusion's efficacy. The suggested algorithm has higher entropy (6.99 bits/pixel for MR-MRA), spatial frequency (SF) (27.95 cycles/millimeter for CT-MRI), edge-based image fusion measure (QAB/F) (0.94 for MRI-PET), and standard deviation (SD) (40.24 for X-ray-VA) as compared to other existing algorithms. The experimental data enlightens that the proposed (UDWT + Fuzzy set) approach outperforms other approaches discussed in the literature.