This paper presents a novel methodology for depression detection using Electroencephalogram (EEG) signals from the Multi-modal Open Dataset for Mental Disorder Analysis (MODMA). The proposed approach integrates advanced signal processing techniques with a Recurrent Neural Network (RNN) classification model to objectively identify depressive states. The methodology consists of three key stages: data preprocessing, feature extraction, and RNN-based classification. EEG data is preprocessed using bandpass filtering, reshaping, and splitting into training and testing sets, preparing it for deep learning model input. The RNN-based model is trained on these features, achieving an accuracy of 77%, outperforming previous studies that used traditional machine learning approaches, such as KNN (K-Nearest Neighbors). Results reveal potential challenges, including model overfitting and class imbalance, suggesting avenues for further optimization. This research highlights the capability of deep learning in mental health diagnostics, providing a data-driven approach that may offer earlier, more precise detection of depression. Future work will focus on enhancing diagnostic accuracy by combining EEG with neuroimaging techniques and exploring broader, more diverse patient populations.