This paper provides a comprehensive overview of positioning techniques in wireless networks (WNs), covering background, applications, measurements, state-of-the-art technologies, and future challenges. Positioning is crucial for emerging applications such as extended reality, unmanned aerial vehicles, and smart environments, requiring both data communication and high-precision positioning. The paper discusses the advantages of WNs over Global Navigation Satellite System (GNSS) in terms of latency, indoor coverage, cost-effectiveness, and enhanced positioning capabilities through technologies like artificial intelligence (AI), large models, and reconfigurable intelligent surfaces (RIS). The evolution of positioning technologies in WNs is traced from cellular networks, highlighting the improvements in accuracy and scalability. Key performance indicators (KPIs) such as accuracy, energy efficiency, availability, cost, latency, scalability, robustness, and security are discussed, along with measurement techniques like Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), Phase of Arrival (POA), Received Signal Strength (RSS), and Channel State Information (CSI). The paper also explores key enabler techniques, including machine learning (ML) for feature extraction and CSI analysis, large models for multimodal data integration, adaptive filters for fusion positioning, and RIS for dynamic signal modification. These techniques aim to enhance the accuracy and robustness of positioning systems in various environments. Finally, the paper outlines the challenges and future directions in the field, emphasizing the need for further research to address real-time requirements, environmental factors, and security issues. The comprehensive review serves as a valuable reference for researchers and practitioners in the field of wireless positioning.This paper provides a comprehensive overview of positioning techniques in wireless networks (WNs), covering background, applications, measurements, state-of-the-art technologies, and future challenges. Positioning is crucial for emerging applications such as extended reality, unmanned aerial vehicles, and smart environments, requiring both data communication and high-precision positioning. The paper discusses the advantages of WNs over Global Navigation Satellite System (GNSS) in terms of latency, indoor coverage, cost-effectiveness, and enhanced positioning capabilities through technologies like artificial intelligence (AI), large models, and reconfigurable intelligent surfaces (RIS). The evolution of positioning technologies in WNs is traced from cellular networks, highlighting the improvements in accuracy and scalability. Key performance indicators (KPIs) such as accuracy, energy efficiency, availability, cost, latency, scalability, robustness, and security are discussed, along with measurement techniques like Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), Phase of Arrival (POA), Received Signal Strength (RSS), and Channel State Information (CSI). The paper also explores key enabler techniques, including machine learning (ML) for feature extraction and CSI analysis, large models for multimodal data integration, adaptive filters for fusion positioning, and RIS for dynamic signal modification. These techniques aim to enhance the accuracy and robustness of positioning systems in various environments. Finally, the paper outlines the challenges and future directions in the field, emphasizing the need for further research to address real-time requirements, environmental factors, and security issues. The comprehensive review serves as a valuable reference for researchers and practitioners in the field of wireless positioning.