Depression, a prevalent adult symptom, can arise from various sources, including mental health conditions and social interactions. With the rise of social media, adults often share their daily experiences, potentially revealing their emotional state on social platforms, like X (formerly Twitter) and Facebook. In this study, we present Ensemble (E) of Convolutional Neural Network (C), Attention-based Long Short-Term Memory (L) Network, and Support Vector Machine (S) (E-CLS), utilizing Term Frequency-Inverse Document Frequency (TF-IDF) vectors, Global Vectors for Word Representation (GloVe) and Bidirectional Encoder Representations from Transformers (BERT) word embeddings. This model effectively identifies depressive posts. Validated with a Twitter-derived depressive dataset, E-CLS achieves an impressive F1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_{1}$$\end{document}-score of 0.91, surpassing existing machine-learning and deep-learning models by 2%. This research advances the detection of depression in social media posts, holding promise for enhanced mental health monitoring. Furthermore, our work contributes to the burgeoning field of mental health informatics by leveraging state-of-the-art techniques in natural language processing. The ensemble approach synergizes the strengths of Convolutional Neural Network (CNN) for local pattern recognition, Long Short-Term Memory (LSTM) Network for sequential context understanding, and Support Vector Machine (SVM) for robust classification. The incorporation of TF-IDF vectors and GloVe embeddings enriches feature representation, enhancing the model's ability to discern nuanced linguistic cues associated with depression. By demonstrating superior performance over established models, E-CLS showcases its potential as a valuable tool in digital mental health interventions.