Depression is a widespread mental health condition affecting millions globally, making early detection and intervention critical for effective treatment and management. With the proliferation of social media platforms, there is increasing interest in utilizing these platforms to identify signs of depression in users. This paper reviews current methods and trends in detecting depression through social media, highlighting key techniques, challenges, and future directions. Depression is a prevalent mental disorder that significantly impacts individuals' mental health and daily lives. It often leads to a loss of interest in regular activities and can result in suicidal thoughts, making it a significant societal issue. Consequently, there is a growing need for automated systems to detect depression across various age groups. Researchers have been exploring effective approaches to identify depression, leading to numerous proposed studies. This study analyzes existing research on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for detecting depression. It examines different methods used to detect emotions and moods in individuals, including facial expressions, images, emotional chatbots, and texts on social media platforms. Techniques such as Naive-Bayes, Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, Radial Neural Networks (RNN), Logistic Regression, and Linear Support Vector Machines are utilized to recognize emotions from text processing. Additionally, Artificial Neural Networks (ANN) are employed for feature extraction and classification of images to detect emotions through facial expressions.