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Depression is a debilitating condition, posing a significant challenge to public mental health globally. Despite advancements in diagnostic techniques, identifying depression remains challenging due to reliance on subjective assessments and diagnostic inaccuracies. These limitations underscore the need for exploring neural biomarkers to enhance diagnosis and treatment strategies. This research addresses the issue by investigating the effective connectivity (EC) patterns in the resting-state Electroencephalography (rsEEG) signals of individuals with Major Depressive Disorder (MDD) compared to healthy controls (HC). Existing studies primarily focus on functional connectivity (FC), often neglecting the causal interactions be-tween brain regions. To overcome this limitation, we apply the Frequency-Domain Convergent Cross Mapping (FD-CCM) technique, a model-free, nonlinear method capable of capturing EC patterns in the frequency domain. The study reveals that MDD subjects exhibit reduced EC across four major brain regions-frontal, parietal, temporal, and occipital-compared to HC participants. Notably, the findings indicate diminished frontal connectivity and altered power density in the delta and alpha frequency bands. Classification results demonstrate that FD-CCM features consistently outperform classical CCM across multiple classifiers, achieving an accuracy of 92.32% with the best-performing ANN classifier. These findings suggest that altered EC patterns are significant biomarkers for MDD, contributing to deficits in cognitive processing, emotional regulation, and sensory integration. The superior performance of the FD-CCM approach highlights its potential for clinical applications in mental health diagnostics.