Positioning using wireless networks (WNs) is a critical enabler for emerging applications such as extended reality (XR), unmanned aerial vehicles (UAVs), and smart environments. These applications require both data communication and high-precision positioning, making WNs ideal for delivering such services. This paper provides a comprehensive overview of existing works and new trends in positioning techniques from both academic and industrial perspectives. It covers the background, applications, measurements, state-of-the-art technologies, and future challenges of positioning in WNs. The paper discusses the key performance indicators (KPIs) and measurements of positioning systems, reviews key enabler techniques such as artificial intelligence (AI), large models, and adaptive systems, and explores various wireless positioning technologies. It also addresses standardization efforts and remaining challenges in the field. Positioning using WNs offers several advantages over GNSS systems, including lower latency, improved coverage in indoor environments, and the reuse of existing WN infrastructure. However, GNSS faces challenges such as long signal propagation times and signal attenuation in indoor environments. To address these, positioning technologies based on WNs, such as cellular networks, WiFi, Bluetooth, and visible light positioning (VLP), are valuable for indoor environments. The evolution of positioning over WNs has seen significant advancements, particularly with the development of 5G and the introduction of new technologies such as millimeter waves (mmWave), terahertz (THz), and optical bands. These technologies offer improved positioning accuracy (PA) for both indoor and outdoor environments. VLP, based on visible light communication (VLC), has shown promise for achieving centimeter-level PA. Other wireless technologies such as WiFi, Bluetooth, RFID, and UWB are also important for future positioning applications. This paper categorizes positioning applications into three types: public provision, enterprise, and individual users. Public provision includes applications such as navigation in airports, museums, and hospitals, as well as medical and healthcare applications that enhance operational efficiency and patient care. Enterprise applications include UAVs, location-based personnel and customer assistance, and asset tracking and management. Individual applications include extended reality (XR), smart life, and transportation services. The paper discusses the key performance indicators (KPIs) of positioning systems, including accuracy, energy efficiency, availability, cost, latency, scalability, robustness, and security. It also reviews various positioning measurement techniques such as time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), phase of arrival (POA), and received signal strength (RSS). These techniques are compared in terms of their accuracy, cost, and suitability for different applications. The paper also explores key enabler techniques such as machine learning (ML), large models, and adaptive filters. ML is used to extract positioning features from wireless signals and improve the accuracy of positioning systems. Large models have the potential to enhance the ability of positioning systems to understand and predict environments, as well as to alignPositioning using wireless networks (WNs) is a critical enabler for emerging applications such as extended reality (XR), unmanned aerial vehicles (UAVs), and smart environments. These applications require both data communication and high-precision positioning, making WNs ideal for delivering such services. This paper provides a comprehensive overview of existing works and new trends in positioning techniques from both academic and industrial perspectives. It covers the background, applications, measurements, state-of-the-art technologies, and future challenges of positioning in WNs. The paper discusses the key performance indicators (KPIs) and measurements of positioning systems, reviews key enabler techniques such as artificial intelligence (AI), large models, and adaptive systems, and explores various wireless positioning technologies. It also addresses standardization efforts and remaining challenges in the field. Positioning using WNs offers several advantages over GNSS systems, including lower latency, improved coverage in indoor environments, and the reuse of existing WN infrastructure. However, GNSS faces challenges such as long signal propagation times and signal attenuation in indoor environments. To address these, positioning technologies based on WNs, such as cellular networks, WiFi, Bluetooth, and visible light positioning (VLP), are valuable for indoor environments. The evolution of positioning over WNs has seen significant advancements, particularly with the development of 5G and the introduction of new technologies such as millimeter waves (mmWave), terahertz (THz), and optical bands. These technologies offer improved positioning accuracy (PA) for both indoor and outdoor environments. VLP, based on visible light communication (VLC), has shown promise for achieving centimeter-level PA. Other wireless technologies such as WiFi, Bluetooth, RFID, and UWB are also important for future positioning applications. This paper categorizes positioning applications into three types: public provision, enterprise, and individual users. Public provision includes applications such as navigation in airports, museums, and hospitals, as well as medical and healthcare applications that enhance operational efficiency and patient care. Enterprise applications include UAVs, location-based personnel and customer assistance, and asset tracking and management. Individual applications include extended reality (XR), smart life, and transportation services. The paper discusses the key performance indicators (KPIs) of positioning systems, including accuracy, energy efficiency, availability, cost, latency, scalability, robustness, and security. It also reviews various positioning measurement techniques such as time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), phase of arrival (POA), and received signal strength (RSS). These techniques are compared in terms of their accuracy, cost, and suitability for different applications. The paper also explores key enabler techniques such as machine learning (ML), large models, and adaptive filters. ML is used to extract positioning features from wireless signals and improve the accuracy of positioning systems. Large models have the potential to enhance the ability of positioning systems to understand and predict environments, as well as to align