Depression is a prevalent mental health disorder affecting millions of people worldwide. Early detection and intervention are crucial for effective treatment and improved outcomes. With the widespread use of social media platforms, individuals often express their thoughts, emotions, and experiences online, providing a potential source of data for identifying signs of depression. This study explores the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques to develop an automated system for detecting depression in social media posts. We collected a large dataset of social media content, including posts from individuals with self-reported depression and a control group. Various NLP techniques were applied to preprocess and extract features from the text data. Multiple machine learning algorithms were then employed to classify posts as indicative of depression or not. The performance of different models was compared, and the best-performing model achieved an accuracy of 89% and an F1-score of 0.87. Our findings demonstrate the potential of leveraging NLP and ML for automated depression detection in social media, which could serve as a valuable tool for early intervention and mental health support.