Medical image fusion plays a crucial role in combining complementary information from multimodal medical images, enhancing diagnostic accuracy and clinical decision-making. This paper presents a novel modified Non-Subsampled Contourlet Transform (NSCT)-based algorithm for enhanced medical image fusion. The proposed method incorporates adaptive fusion rules designed to maximize detail preservation, structural similarity, and edge retention while maintaining computational efficiency. Comprehensive experiments were conducted on multiple imaging modalities, including Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Magnetic Resonance Angiography (MRA), and Single Photon Emission Computed Tomography (SPECT), and evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Entropy (EN), and Edge Preservation Index (EPI). The results demonstrate that the proposed method consistently outperforms traditional fusion techniques, delivering superior fusion quality and robustness across modalities.