Purpose. Deep Learning (DL) has become a potent tool in the constantly changing field of mental health, helping to solve the complex puzzle of diseases like depression. Traditionally, identifying vulnerability to depression has relied on surveys and human judgment, often missing critical nuances. DL, inspired by the human brain, transcends these limitations by uncovering hidden patterns across diverse data sources, from social media to genetics. This groundbreaking approach facilitates the early detection of depression, transcending cultural boundaries. Nevertheless, ethical considerations and privacy concerns must remain paramount as we harness the potential of DL. It is a shining light that aids our understanding and prediction of depression but should augment, not supplant human empathy. Depression, afflicting over 264 million people globally, is a pressing concern with significant societal implications. Its core symptoms, including sadness and anhedonia, profoundly impact individuals’ lives and can, in severe cases, lead to suicide. One challenge in addressing depression is the difficulty in tracking it effectively and predicting relapse. Machine learning and deep learning models analyze these multidimensional EEG signals to differentiate between individuals vulnerable to depression and their healthy counterparts. Several researchers have used machine learning and resting-state EEG data to predict Major Depressive Disorder (MDD). Methods. This study employs a diverse dataset and considers the EEG signals of normal subjects, depressed subjects, and subjects recovered from depression. The study highlights a variety of plots to demonstrate the differences between these subjects, then compares various machine learning algorithms and employs a novel methodology to evaluate the resting-state EEG data in distinguishing between people who are highly and lowly vulnerable to depression. Findings. This study demonstrated the exceptional performance of the proposed DL ensemble model, which combines 1D CNN and LSTM, achieving the best results (Accuracy = 99%, AUC-ROC = 99%, AUC-PR = 98%, Log Loss = 19%, Hamming Loss = 1%, Jaccard Similarity Coefficient = 97%, Brier Score = 1%, Mean Square Error = 1%, Mean Absolute Error = 1%, R-Squared Error = 95%) when compared with other machine learning and deep learning models. Conclusion. This study highlights the central role of deep learning (DL) in transforming the analysis of mental health, especially in the context of depression. Using DL techniques, we can overcome the limitations of traditional methods based on surveys and human assessment, enabling early detection and intervention of depression in various populations and cultural contexts. But as we exploit the potential of DL, it is necessary to adhere to ethical standards and prioritize privacy considerations. DL is a sign of hope in understanding and predicting depression, complementing rather than replacing human empathy. Since depression affects millions worldwide and represents significant social challenges, the results of this study demonstrating the exceptional performance of the proposed DL item model offer promising opportunities to improve diagnostic accuracy and ultimately improve mental health outcomes.