This review article discusses the role of artificial intelligence (AI) in personalized pain medicine, focusing on sensors and devices that enhance pain monitoring, assessment, and treatment. Personalized pain medicine aims to tailor treatment strategies to individual patient needs, improving outcomes, reducing side effects, and enhancing patient satisfaction. Despite existing pain markers and treatments, challenges remain in understanding, detecting, and treating complex pain conditions. The article reviews recent engineering efforts in developing various sensors and devices for addressing these challenges. It summarizes the basics of pain pathology and introduces various sensors and devices for pain monitoring, assessment, and relief. It also discusses advancements in medical AI, such as AI-based analgesia devices, wearable sensors, and healthcare systems. These technologies may lead to more precise and responsive personalized medicine, improving patient quality of life, increasing medical system efficiency, and reducing addiction and substance use disorders. Pain is a complex and subjective experience that significantly impacts quality of life and healthcare systems. It is a primary reason for seeking medical care, with conditions like osteoarthritis, back pain, and headaches being among the top causes for patient visits. Pain is classified into acute, episodic, and chronic types, with chronic pain persisting beyond three months and often having minimal evolutionary benefit. Pain can be categorized by its source into nociceptive, neuropathic, and nociplastic pain. Chronic pain conditions, particularly those related to cancer and spinal disorders, often have mixed pain phenotypes. Sensors and devices for pain sensing include wearable physical/chemical sensors that measure physiological and biochemical indicators, providing insights into individual pain response patterns. Intelligent wearable sensors enhance pain assessments and facilitate real-time data transmission to smartphones or computers, enabling remote monitoring and management. Imaging devices such as fMRI, PET, CT, and ultrasound provide insights into pain conditions but face challenges in cost, radiation exposure, and image quality. Intelligent communication devices, leveraging AI and LLMs, are pioneering new pathways for pain assessment and psychological evaluation. These devices can provide multilingual communication flexibility but require strict confidentiality and security measures. Biomarkers for pain are essential for accurate assessment and treatment. Integrating multiple biomarkers can enhance clinical decision-making and support personalized pain management. Implantable drug pumps and other analgesia devices offer effective solutions for chronic pain, but they come with surgical and infection risks. Noninvasive analgesia devices like TENS, TMS, TCS, and ultrasonic technology provide alternative treatments with varying degrees of effectiveness. AI is rapidly becoming essential in personalized pain medicine, enabling continuous and objective pain assessment through data analytics and machine learning. AI-guided smart drug delivery systems can autonomously adjust medication dosages, reducing risks associated with drug dependency and side effects. Challenges in personalized pain medicine include the subjectivity of pain, unequal distribution of medical resources, and substance abuse and addiction. Future developments in AI-based analgesia devices and continuous monitoring systems aim to improve pain management through personalized treatment plans and realThis review article discusses the role of artificial intelligence (AI) in personalized pain medicine, focusing on sensors and devices that enhance pain monitoring, assessment, and treatment. Personalized pain medicine aims to tailor treatment strategies to individual patient needs, improving outcomes, reducing side effects, and enhancing patient satisfaction. Despite existing pain markers and treatments, challenges remain in understanding, detecting, and treating complex pain conditions. The article reviews recent engineering efforts in developing various sensors and devices for addressing these challenges. It summarizes the basics of pain pathology and introduces various sensors and devices for pain monitoring, assessment, and relief. It also discusses advancements in medical AI, such as AI-based analgesia devices, wearable sensors, and healthcare systems. These technologies may lead to more precise and responsive personalized medicine, improving patient quality of life, increasing medical system efficiency, and reducing addiction and substance use disorders. Pain is a complex and subjective experience that significantly impacts quality of life and healthcare systems. It is a primary reason for seeking medical care, with conditions like osteoarthritis, back pain, and headaches being among the top causes for patient visits. Pain is classified into acute, episodic, and chronic types, with chronic pain persisting beyond three months and often having minimal evolutionary benefit. Pain can be categorized by its source into nociceptive, neuropathic, and nociplastic pain. Chronic pain conditions, particularly those related to cancer and spinal disorders, often have mixed pain phenotypes. Sensors and devices for pain sensing include wearable physical/chemical sensors that measure physiological and biochemical indicators, providing insights into individual pain response patterns. Intelligent wearable sensors enhance pain assessments and facilitate real-time data transmission to smartphones or computers, enabling remote monitoring and management. Imaging devices such as fMRI, PET, CT, and ultrasound provide insights into pain conditions but face challenges in cost, radiation exposure, and image quality. Intelligent communication devices, leveraging AI and LLMs, are pioneering new pathways for pain assessment and psychological evaluation. These devices can provide multilingual communication flexibility but require strict confidentiality and security measures. Biomarkers for pain are essential for accurate assessment and treatment. Integrating multiple biomarkers can enhance clinical decision-making and support personalized pain management. Implantable drug pumps and other analgesia devices offer effective solutions for chronic pain, but they come with surgical and infection risks. Noninvasive analgesia devices like TENS, TMS, TCS, and ultrasonic technology provide alternative treatments with varying degrees of effectiveness. AI is rapidly becoming essential in personalized pain medicine, enabling continuous and objective pain assessment through data analytics and machine learning. AI-guided smart drug delivery systems can autonomously adjust medication dosages, reducing risks associated with drug dependency and side effects. Challenges in personalized pain medicine include the subjectivity of pain, unequal distribution of medical resources, and substance abuse and addiction. Future developments in AI-based analgesia devices and continuous monitoring systems aim to improve pain management through personalized treatment plans and real