<![CDATA[Objective pain detection is a major challenge in the medical field because pain is subjective and difficult to measure accurately, particularly in patients who are unable to communicate. This study aims to identify and analyze both sensor-based and non-sensor technologies used in pain detection, as well as to evaluate technological development trends in this domain. The methodology employed is a Systematic Literature Review (SLR) following the PRISMA protocol, involving rigorous search and selection processes across the Scopus, PubMed, and Google Scholar databases. Out of a total of 3,559 identified articles, 130 studies were selected after screening and quality assessment. All eligible articles were then analyzed by extracting information relevant to the research topic. The findings indicate that physiological sensors such as EDA, ECG, EMG, and EEG, along with non-sensor technologies such as facial expression analysis and hybrid approaches combining physiological and non-sensor methods, represent the primary strategies for pain detection. Current trends include the adoption of wearable devices, federated learning, and explainable AI. Moreover, sensor technologies play a pivotal role in healthcare by integrating diverse physiological and behavioral data to support automated decision-making, thereby enhancing efficiency and accuracy in pain diagnosis. This study recommends the development of pain detection systems that are more accurate, adaptive, and ethical, as well as clinical trials in real-world settings to improve the validity and acceptance of these technologies in medical practice.]]>
