Post-traumatic stress disorder (PTSD) is a complex psychological condition that affects a significant portion of the population following exposure to traumatic events. This study explores the application of machine learning (ML) techniques in the detection, prediction, and understanding of post-traumatic stress disorder (PTSD). Various ML models, including supervised learning, deep learning, and natural language processing, are reviewed for their effectiveness in analyzing diverse data sources such as physiological signals, clinical notes, and social media content. The review emphasizes the potential of ML to enhance PTSD diagnostics and treatment while addressing challenges such as small sample sizes, computational costs, and generalizability.