###### Abstract  
The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance. 10 2024 24 482  
technologies [3]. These technologies have emerged as a viable option for meeting the unique requirements of IoT applications. These technologies offer advantages such as extended transmission ranges, low energy consumption, and cost-effective implementation options. LPWANs offer an affordable way for machine-to-machine (M2M) connectivity in addition to enabling and providing low-power and long-range communication [4]. These technologies can use these capabilities, and because of that, devices with battery lives of years or even decades can be deployed. LPWAN technology has paved the way for new opportunities in IoT applications, particularly in places like rural areas where cellular network coverage is limited [5]. Although LPWANs are being used in a variety of industrial sectors, it is interesting to note that the majority of public LoRaWAN gateways are in metropolitan regions [6; 7]. Numerous applications are supported by these gateways, such as waste management, smart metering, weather stations, smart parking, flood monitoring, and infrastructure monitoring. The strategic placement of LoRaWAN gateways in urban environments is in accordance with the strengths and complements of the technology as well as the specific needs of these applications [8]. The two most widely used LPWAN applications are Sigfox and LoRaWAN, with LoRaWAN operating as an open standard for the network and a link layer above the LoRa physical layer [8]. Sigfox was the first LPWAN specifically created for the IoT back in 2019. Recent years have also seen the emergence of novel LPWAN technologies that use unlicensed radio bands for low-power, long-range transmission, such as Symphony Link, Weightless, and Ingenu RPMA. In comparison with technologies such as ZigBee and BLE, which have used mesh topologies for extended communication, LPWAN technologies like Sigfox, LoRaWAN, and NB-IoT have used a star topology in which the end devices are directly connected to public base stations [3]. This approach eliminates the need for connected end-devices to listen to the radio channel before transmitting, and this optimizes energy consumption.