With the swift development of deep generative models, it is now possible to produce extremely realistic synthetic audio-visual content often referred to as deepfakes that is posing significant risks to digital trust, security and media authenticity. Despite the reported significant progress in recent deepfake detection algorithms, most of the existing systems are densely based on the spatial or temporal characteristics and cannot to generalize against state-of-the-art generative models, particularly diffusion-based methods. In addition, existing multimodal models do not address much about frequency-domain inconsistency and inter-modal spectral associations that occur during media manipulation. In order to curb such constraints, the present paper suggests a new cross-frequency multi-modal deepfake detector that jointly trains based on audio and visual cues in the frequency domain. The suggested approach breaks down both modalities into multi-band spectral feature and trains the cross-frequency associations between the respective audio and visual elements. The framework manages to capture minute manipulation artifacts that are normally invisible on the space-wise domain by modelling inter-modal spectral alignment with the aid of a cross-frequency correlation and attention mechanism. The results of extensive experiments developed on several benchmark datasets (FF++, Celeb-DF, DFDC, FakeAVCeleb, and WaveFake) show that the presented approach is better than the existing unimodal and multimodal ones in terms of accuracy, robustness, and generalization. The findings confirm that cross frequency reasoning offers a robust and resilient cue when next generation deep fake detection is needed especially when compression, noise, and invisible manipulating are involved.