This paper presents a hybrid medical image fusion (MIF) technique (SWT-PnP-DnCNN) that combines multiscale decomposition, spatial-frequency-driven fusion, and deep denoising priors to efficiently integrate MIF images. The SWT-PnP-DnCNN begins with the Stationary Wavelet Transform (SWT) to decompose input medical images into low-frequency (LFSBs) and high-frequency (HFSBs) subbands. The LFSBs are fused using spatial frequency-based weighted averaging, effectively integrating overall intensity and contrast information. For the HFSBs, a local energy and max-selection strategy is adopted to retain salient edge features from the source images. Following the initial fusion, a Plug-and-Play (PnP) optimization strategy is applied to improve this fused image. This step uses a pretrained DnCNN model as a deep denoiser, serving as an implicit image prior in a model-driven iterative framework. Each iteration alternates between a data consistency step and a denoising step, significantly reducing artifacts and enhancing structural fidelity in the result. The effectiveness of SWT-PnP-DnCNN is demonstrated on benchmark CT-MRI, MRI-PET, and MRI-PET datasets. Extensive evaluation against classical hybrid strategies and recent CNN-based fusion methods shows that SWT-PnP-DnCNN achieves the best performance across standard metrics. We further include mean±std reporting and paired t-tests, confirming statistically significant improvements (p < 0.05). Ablation studies validate each design choice by comparing SWT-only vs. SWT+PnP and evaluating denoiser alternatives, with sensitivity to PnP iterations, regularization strength, and SWT levels. The runtime analysis clarifies feasible deployment, particularly in offline or cloud-based environments. Overall, SWT-PnP-DnCNN emerges as a robust, interpretable, and clinically valuable solution for enhancing MIF in medical imaging applications.