Accurate flight trajectory prediction is crucial for enhancing the overall efficiency of air traffic management. However, existing methods often overlook the importance of capturing flight trends and pay insufficient attention to temporal relationships between different variate channels, impacting prediction accuracy. In this work, we propose a novel Multi Channel-Frequency Mamba-based framework MCFM, which leverages Mamba for the first time to extract flight trends from frequency information and incorporates time dependencies of diverse channels. Specifically, MCFM utilizes the Multi-Channel Interaction block to extract and fuse the temporal patterns of various channels. We design the Attention Mamba to perform frequency extraction for obtaining detailed trend insights. On that basis, we introduce a Shared Mamba to increase the interaction of frequency information, thus adding mutual guidance and improving prediction accuracy. Experimental results demonstrate that MCFM outperforms existing methods on a real-world dataset.