The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is reshaping agricultural water management through the development of Smart Irrigation Systems (SIS). This systematic review explores how the synergy between AI and IoT enhances irrigation efficiency, with a particular focus on identifying recent technological advancements and future research opportunities. These systems also confront significant obstacles, such as high infrastructure costs, sensor dependability, and model correctness. In this research, 12 chosen publications published between 2020 and 2025 are analyzed as part of a systematic literature review (SLR) on AI- and IoT-based SIS. Three primary areas are the focus of the study: IoT technologies and sensors used, AI techniques applied, and irrigation optimization strategies. According to our research, the most popular IoT technologies are NB-IoT, Wi-Fi, and LoRaWAN. However, the best AI models for estimating soil moisture and maximizing irrigation schedules are LSTM, Random Forest, and XGBoost. Though there are still drawbacks, especially with regard to sensor prices, frequent model calibration, and dependence on weather data, AI models also perform better than conventional methods. The study’s findings underscore the critical role of AI and IoT in optimizing irrigation systems, while also addressing the barriers to large-scale deployment. Lastly, we suggest future research directions that center on enhancing model robustness, creating energy-efficient solutions for intelligent and sustainable irrigation, and integrating AI with next-generation IoT sensors.