## 1 Introduction  
Vibrant advancements in modern technology have expanded living areas and necessitated the identification of users' locations in real time. Traditional methods, such as compasses and maps, have been supplanted by contemporary alternatives, e.g., global positioning systems (GPS). The global navigation satellite system (GNSS), which employs satellites to transmit users' real-time locations, has enabled location-based systems (LBS) in fields, e.g., aircraft, ships, and private vehicles, resulting in enhanced user convenience. The emergence of state-of-the-art technologies, e.g., autonomous driving and virtual reality, necessitate precise, real-time user location data to guide users to their desired destinations and enhance the quality of their experiences. Thus, it is obvious that localization technology is essential to the use of various LBS services.  
During the initial phases of development, location measurement was limited to rough approximations in outdoor environments due to the sole accessibility of GPS or GNSS. However, present-day research focuses on developing ultra-precise localization systems that can extend services to indoor environments and minimize errors from meters to centimeters. In scenarios where satellite signals cannot be accessed, an indoor positioning system (IPS) has become necessary for accurately determining location within indoor or underground spaces [1, 2]. Since most of our daily activities occur indoors, IPS can be utilized in various fields such as guiding individuals within buildings, tracking emergency patients' locations, inventory management in retail stores and warehouses, and more.  
As mentioned above, the demand for LBS is increasing nowadays in various ways. Consequently, the advancement of localization technology must keep pace with evolving requirements, a demand well-matched by the rapid progress of mobile communication systems such as cellular and Wi-Fi. However, besides the primary goal of improving wireless communication system specifications, wireless localization encounters its own set of challenges. These include environmental interference, accuracy limitations in densely populated spaces, and potential privacy implications stemming from the collection of location data.  
For this reason, active research on artificial intelligence (AI) has also made significant contributions in this regard [3; 4; 5], which can extract unveiled patterns or hidden features to solve exceedingly complex problems in a simpler manner. More specifically, machine learning is a family of algorithms which are developed through data to enable computers to learn and identify patterns, unlike traditional explicit programming where developers implement the algorithms themselves. Furthermore, deep learning, a special subset of machine learning, uses artificial neural networks (ANNs) that emulate the human brain to extract statistical characteristics of data. These AI techniques are expected to have new roles, such as extracting features that are difficult to achieve through conventional mobile communication system optimization or significantly reducing complexity. Consequently, when combined with localization, these techniques can enhance measurement processes to improve localization accuracy, and resource utilization efficiency, and overcome existing limitations of localization technology.  
In this paper, we briefly introduce the wireless communication technologies capable of inducing wireless localization. Next, we outline wireless localization techniques and define the factors that hinder their performance. Additionally, we categorize machine learning algorithms and provide examples of how they can be implemented to mitigate these obstacles. Lastly, we propose potential future research directions and prospects for wireless localization.