Accurate recognition of micrometeorological conditions along overhead transmission lines is essential for the safe operation of power grids. However, meteorological cues such as snowfall, haze, and rainfall are often distributed across an image at different spatial scales and may exhibit similar visual appearances. To address these challenges, this study proposes MMRNet, a ConvNeXt-based multimodal model for meteorological condition recognition. First, a Scale-Aware Channel Mixing (SACM) module is designed to model spatially dispersed features. Second, a Cross-Scale Transformer Fusion (CSTF) module is introduced to improve semantic consistency across multiple scales. Finally, a Numeric-Integrated Visual Enhancement (NIVE) module fuses time-series meteorological measurements with visual features. A dataset containing 3950 transmission-line images and their corresponding sequences of five hourly meteorological observations was constructed for evaluation. Experimental results show that each module improves recognition performance and that their combined use achieves the highest accuracy. The complete model achieves 96.33% accuracy with 21.55 million parameters and 2.92 GFLOPs, balancing recognition performance and computational cost. Independent field evaluation achieved 96.0% recall for low-quality images, with recognition metrics decreasing by 0.5–0.8 percentage points on images meeting the quality requirements relative to the original validation results.