This systematic review explores the current state of technologies and methodologies for monitoring and analyzing physical and cognitive ergonomics in the context of Industry 5.0, with a focus on worker well-being. The study addresses three research questions: (1) What technologies are used to assess physical and cognitive well-being in industrial workplaces? (2) How are the collected data processed? (3) What is the purpose of assessing worker well-being in Industry 5.0? The review identifies the most commonly used technologies, parameters, and data processing methods from 65 articles. Wearable inertial measurement units (IMUs) and RGB-D cameras are the most prevalent devices for physical monitoring, while cardiac activity is the most commonly used physiological parameter for cognitive ergonomics. The study highlights the importance of integrating these technologies with AI techniques, such as machine learning (ML) and deep learning (DL), to analyze data and improve worker well-being. The findings emphasize the need for multi-modal systems that combine physical and cognitive ergonomics to support human-centered manufacturing. The review also discusses the practical challenges and future directions for research in this area, including the development of real-time monitoring systems and the integration of AI with wearable sensors to enhance worker safety and productivity. The study concludes that a holistic approach to ergonomics, considering both physical and cognitive aspects, is essential for creating safe and efficient workplaces in Industry 5.0.This systematic review explores the current state of technologies and methodologies for monitoring and analyzing physical and cognitive ergonomics in the context of Industry 5.0, with a focus on worker well-being. The study addresses three research questions: (1) What technologies are used to assess physical and cognitive well-being in industrial workplaces? (2) How are the collected data processed? (3) What is the purpose of assessing worker well-being in Industry 5.0? The review identifies the most commonly used technologies, parameters, and data processing methods from 65 articles. Wearable inertial measurement units (IMUs) and RGB-D cameras are the most prevalent devices for physical monitoring, while cardiac activity is the most commonly used physiological parameter for cognitive ergonomics. The study highlights the importance of integrating these technologies with AI techniques, such as machine learning (ML) and deep learning (DL), to analyze data and improve worker well-being. The findings emphasize the need for multi-modal systems that combine physical and cognitive ergonomics to support human-centered manufacturing. The review also discusses the practical challenges and future directions for research in this area, including the development of real-time monitoring systems and the integration of AI with wearable sensors to enhance worker safety and productivity. The study concludes that a holistic approach to ergonomics, considering both physical and cognitive aspects, is essential for creating safe and efficient workplaces in Industry 5.0.