Reconfigurable Intelligent Surfaces (RISs), also known as Intelligent Reflecting Surfaces (IRSs) or Large Intelligent Surfaces (LISs), are promising technologies for 6G networks due to their ability to enhance wireless network capacity and coverage by smartly reconfiguring the wireless propagation environment. This paper provides a comprehensive overview of RISs, focusing on their operating principles, performance evaluation, beamforming design, resource management, and integration with emerging technologies like machine learning. It discusses the basic principles of RISs from physics and communications perspectives, presents performance evaluations of multi-antenna assisted RIS systems, and systematically surveys existing designs for RIS-enhanced wireless networks. The paper also explores the application of machine learning to RIS-enhanced wireless networks and identifies major issues and research opportunities in integrating RISs with other emerging technologies for next-generation networks. RISs are nearly-passive devices made of electromagnetic materials, capable of reconfiguring the wireless propagation environment by controlling the phase shift of each reflecting element. They offer advantages such as easy deployment, spectral efficiency enhancement, environmental friendliness, compatibility, and the ability to support full-duplex and full-band transmission. RISs can be deployed in various environments, including cellular networks, indoor communications, unmanned systems, and IoT networks, to improve signal quality, reduce latency, and enhance connectivity. The paper also discusses the differences between ray-optics and wave-optics perspectives in analyzing RISs, the importance of wave-optics for power flow analysis, and the tunability of RISs through patch-array implementations. It highlights the challenges and opportunities in RIS design, including hardware limitations, system design simplifications, and optimization limitations. The paper concludes that RISs are a key enabler for future 6G networks, with significant potential for enhancing communication performance and supporting a wide range of applications.Reconfigurable Intelligent Surfaces (RISs), also known as Intelligent Reflecting Surfaces (IRSs) or Large Intelligent Surfaces (LISs), are promising technologies for 6G networks due to their ability to enhance wireless network capacity and coverage by smartly reconfiguring the wireless propagation environment. This paper provides a comprehensive overview of RISs, focusing on their operating principles, performance evaluation, beamforming design, resource management, and integration with emerging technologies like machine learning. It discusses the basic principles of RISs from physics and communications perspectives, presents performance evaluations of multi-antenna assisted RIS systems, and systematically surveys existing designs for RIS-enhanced wireless networks. The paper also explores the application of machine learning to RIS-enhanced wireless networks and identifies major issues and research opportunities in integrating RISs with other emerging technologies for next-generation networks. RISs are nearly-passive devices made of electromagnetic materials, capable of reconfiguring the wireless propagation environment by controlling the phase shift of each reflecting element. They offer advantages such as easy deployment, spectral efficiency enhancement, environmental friendliness, compatibility, and the ability to support full-duplex and full-band transmission. RISs can be deployed in various environments, including cellular networks, indoor communications, unmanned systems, and IoT networks, to improve signal quality, reduce latency, and enhance connectivity. The paper also discusses the differences between ray-optics and wave-optics perspectives in analyzing RISs, the importance of wave-optics for power flow analysis, and the tunability of RISs through patch-array implementations. It highlights the challenges and opportunities in RIS design, including hardware limitations, system design simplifications, and optimization limitations. The paper concludes that RISs are a key enabler for future 6G networks, with significant potential for enhancing communication performance and supporting a wide range of applications.