Depression, a prevalent and debilitating mental health disorder, affects millions of individuals worldwide, profoundly impacting their quality of life. Early identification and diagnosis of depression and its intensity are crucial for effective treatment and management. However, many people with depression do not seek professional help, especially in the early stages. In recent years, social media platforms like Twitter have gained popularity as spaces for sharing personal thoughts and emotions, including sensitive signals indicative of serious issues such as self-harm, suicidal thoughts, or illegal activities. These signals may help us to identify depression-related tweets and determine whether an individual is suffering from depression. This research focuses on utilizing artificial intelligence and deep learning (DL) models to categorize tweets related to depression and measure its intensity. The proposed approach combines emotional features, topical events, and behavioral-biometric signals to train the long short-term memory (LSTM)-based DL models. To create a comprehensive dataset, we collaborated with an expert psychologist who followed the clinical assessment procedure outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5-TR) to label 95 322 tweets as “non-depressed” or “depressed,” further categorized into “mild,” “moderate,” and “severe” intensity levels. Through a series of experiments, our proposed method achieved superior performance compared to baseline models, yielding a mean squared error of 0.002336 and the highest R2 value of 0.61. These results highlight the accuracy and potential applications of our approach in automatic depression screening and monitoring on social media platforms.