## 7 Discussion  
The findings of this review highlighted both the advancements and ongoing challenges in water leak detection technologies, emphasizing the need for continuous innovation. Traditional methods, such as acoustic monitoring and manual surveys, remain useful for small-scale operations due to their relatively low cost and straightforward implementation. However, these methods show significant limitations in terms of real-time detection, accuracy in complex networks, and their dependence on environmental conditions. For instance, studies conducted by [PERSON] et al. [9] and [PERSON] et al. [126] showed that acoustic methods are primarily effective in metallic pipelines but struggle to detect leaks in plastic pipes due to the sound absorption properties of such materials. Furthermore, traditional methods are labor-intensive and prone to operator error, while environmental noise can lead to false positives or missed leaks. Environmental factors, such as soil conditions, temperature fluctuations, and external vibrations from urban infrastructure, further complicate the use of non-invasive methods, such as thermography and ground-penetrating radar. These environmental variables can produce misleading signals, leading to false alarms or missed leaks. Similar findings from [PERSON] et al. [127] support the need for integrated approaches, as combining multiple technologies--such as thermographic data with GPR--could mitigate some of these issues, improving detection accuracy under diverse conditions. In contrast, modern technologies, including inline systems such as SmartBall and fiber optic sensors, offer high precision and continuous monitoring, making them ideal for large-scale or high-pressure networks. Similar to the findings of [PERSON] and [PERSON] [49], who also emphasized the strengths of inline technologies in specific contexts, this review recognized that high costs and complexity limit widespread adoption, particularly in developing regions, where financial constraints and lack of infrastructure pose significant challenges. Additionally, the need for skilled operators to manage these technologies creates barriers for regions with limited technical expertise. The application of artificial intelligence (AI) and machine learning (ML) in water leak detection introduces new possibilities for enhanced accuracy and automation. Techniques such as support vector machines (SVMs) and deep learning models enable the analysis of large datasets, facilitating more precise leak detection and localization. Similar investigations by [PERSON] et al. [11] affirmed the growing importance of AI-driven systems, yet these technologies require access to high-quality, labeled datasets, which may not be readily available in many regions. Additionally, the computational demands of these methods can limit their use in real-time applications, particularly for utilities with limited processing capabilities. Future research should focus on optimizing AI models to function effectively with smaller datasets and developing real-time algorithms that reduce the computational load. Smart water networks represent a promising approach to water management, integrating sensors, data analytics, and real-time monitoring to provide actionable insights into leak detection and system performance. However, the high implementation costs, cybersecurity risks, and lack of standardization across different sensor technologies remain significant barriers to large-scale deployment. Without established standards for interoperability, integrating different sensor technologies within a single system becomes complex,increasing both costs and operational challenges. Furthermore, the rising dependence on interconnected systems raises concerns about cybersecurity vulnerabilities, as attacks on critical infrastructure could disrupt water supplies or compromise sensitive data. An important issue to address is the inequality in access to these advanced technologies.