Social media platforms allow individuals to express their psychological hardship and discuss their methods of managing distress. Thus, by examining people’s discussions and personal inner struggles across platforms such as YouTube, Reddit, and Twitter, we gather data and apply sentiment analysis for the early detection of possible symptoms. This research can help alleviate the risk of self-harm and provide support to reduce the likelihood of suicidal ideation. This research explores the use of sentiment analysis on social media platforms to predict symptoms of depression, Post-Traumatic Stress Disorder, Attention Deficit Hyperactivity Disorder, childhood trauma, stress, and suicidal thoughts. It also aids to identify genuine mental health concerns while filtering out irrelevant negative-sounding words and slang that do not require attention for mental recovery. This study employs transformer architecture models (RoBERTa, BioBERT), deep learning algorithms (LSTM), and other lexicon-based models (TextBlob, VADER and Afinn). The dataset is expertly selected and compiled from multi-platform social media content and annotated for clinically relevant indicators. Preliminary findings suggest transformer models provide superior performance in distinguishing distress-related sentiment. This study contributes a more reliable sentiment detection framework that can inform early intervention tools and aid mental health professionals in identifying at-risk individuals.