Multi-modal medical image fusion plays a crucial role in improving clinical decision-making and boosting diagnostic accuracy by combining comprehensive features from more than one modality. This paper explores the medical image fusion of three different modalities—MRI-gad/MRI-T1/MRI-T2, PET, and CT using three different low-frequency fusion rules—random weighted average, sliding window, and maximum selection. Each rule is evaluated based on its ability to preserve the most salient features from the source images. This study also presents an optimized weighted average fusion rule for the high-frequency coefficients. This approach employs the three most potent metaheuristic optimization algorithms—Differential Evolution, Particle Swarm Optimization, and Genetic Algorithm. These optimization methods aim to dynamically adapt the fusion weights to guarantee the most pertinent and instructive features in the final fused image. The outcome of this study is to present a comparative analysis for the advancement of multi-modal medical image fusion, contributing to improved medical diagnostics, treatment planning, and disease management.