The study introduces a new approach for the enhanced analysis of diseases by fusing multimodal medical images through a hybrid methodology, combining various transform techniques. By leveraging mathematical transformations like wavelet and Fourier transforms, this method integrates information from Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, and X-rays, ensuring a comprehensive disease analysis. The algorithm, incorporating machine learning for automated feature extraction and pattern recognition, produces a unified representation of patients’ conditions. The proposed algorithm was applied on several datasets of medical images and the results demonstrate a significant enhancement in diagnostic accuracy, emphasizing the effectiveness of this hybrid approach in capturing complementary information from different imaging modalities. With broad applications across medical disciplines, this approach promises to be a valuable tool for clinicians, significantly advancing disease detection and monitoring, ultimately contributing to more precise and personalized healthcare.