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Magnetic resonance imaging provides detailed visualization of healthy and abnormal tissues, making it an essential tool for accurate diagnosis. Recent advancements in MRI Image super-resolution networks have shown promising potential. However, the effective aggregation of multi-scale textural details and high-frequency information, which is critical to achieving accurate reconstruction and subsequent clinical applications, remains a significant challenge. To address this limitation, we propose a Multi-scale Feature Aggregation Network (MFAN) for brain MRI image super-resolution. To ensure the selection of the most informative feature channels and spatially significant regions, the proposed network incorporates Channel and Spatial Attention (CSA) mechanisms for shallow feature extraction. In addition, we introduce a Multi-scale Feature Aggregation Attention Block (MFAAB), which extracts and fuses diverse features from multiple pathways, thereby enabling more accurate MRI reconstruction and enhancing the reliability of clinical diagnoses. Experimental results demonstrate that MFAN surpasses state-of-the-art methods on the BraTS 2018 and Brain Tumor datasets. Specifically, on the BraTS 2018 dataset, our model achieves PSNR improvements of 1.054 dB and 0.609 dB and SSIM gains of 0.0128 and 0.0059 at ×2 and ×4 magnifications, respectively.Clinical relevance- The proposed MFAN offers a substantial advancement in brain MRI image super-resolution, positioned to address critical challenges in clinical neuroimaging. Accurate reconstruction of high-resolution images is vital for the reliable detection and diagnosis. By effectively aggregating multiscale textural information and enhancing structural details, MFAN improves diagnostic precision while reducing reliance on repeated scans or high-field MRI systems.