This work is to elevate the quality of fusion process, which includes integrating diverse modalities from distinct sources to create a resultant image helpful for diagnosis. Medical photos frequently exhibit intricate structures that are challenging to distinguish, resulting in blurring and delays in the merged image. Advanced approaches are required to fuse images due to their complexity. The suggested methodology integrates fuzzy sets and the undecimated discrete wavelet transforms (UDWT). Fuzzy sets are used to decrease uncertainty in medical images, while UDWT, a non-orthogonal multi-resolution decomposition technique, is applied for image fusion without a decimation phase. The UDWT domain employs the number of selection criterion to merge low-frequency sub bands, whereas the directional/modified Spatial Frequency (MSF) approach is employed for high-frequency sub bands. Subsequently, the inverse UDWT is employed to get the ultimate combined image. The advantages of the implemented approach are assessed by different metrics, including entropy, spatial frequency, and standard deviation. The experimental results exhibit a higher level of performance in comparison to the algorithms that currently exist. The suggested method demonstrates superior spatial frequency, entropy, and standard deviation when compared to other current methods, as evidenced by experimental data. In conclusion, the study shows that combining fuzzy sets with UDWT makes big steps forward in combining various types of medical images. This leads to better visual analysis and less blurring in the combined images.