The research proposes an intelligent system for diagnosing Major Depressive Disorder (MDD) by employing multiple clustering methods. After pre-processing, the raw EEG is used to extract three quantitative biomarkers (beta, delta, and theta band powers) and three signal-derived features (Detrended Fluctuation Analysis (DFA), Higuchi’s Fractal Dimension (HFD), and Lempel–Ziv Complexity (LZC). The application of different clustering techniques to the analysis of depression data is covered in detail in this article. The author assesses each algorithm’s performance in clustering depression-related data points using a battery of rigorous tests, such as DBSCAN, spectral clustering, K-means, and metric clustering. Through the utilization of several measures, a thorough examination of the clustering outcomes illuminates the advantages and disadvantages of every method in terms of identifying fundamental patterns present in the dataset. The applicability of various clustering techniques in the context of depression data analysis is studied, which also provides insights that may help in the development of more efficient methods for mental health disorder diagnosis and treatment.