In this work, we introduce a novel deep learning architecture, the Quantum Dilated Fourier Convolutional Neural Network (QF-BDL), designed to enhance image recognition performance by combining the strengths of quantum-inspired dilation mechanisms and Fourier spectral representations. Traditional convolutional networks often struggle to capture the Images of long-range dependencies and frequency-domain features simultaneously. To address this, QF-BDL integrates dilated convolutions to expand receptive fields without increasing computational complexity and applies Fourier transformations to encode frequency-specific patterns more effectively. This hybrid approach enables the model to extract both localized textures and global contextual cues, resulting in improved classification accuracy and robustness. Experimental evaluations on benchmark datasets demonstrate that QF-BDL outperforms standard CNNs and quantum dilated models in terms of accuracy and generalization, highlighting its potential in real-world visual recognition tasks. Index Terms Component, Formatting, Styling, Insert.