This study presents the design and implementation of an artificial intelligence-based smart irrigation system, grounded on the analysis of climate conditions in the Thrace Region. The system utilizes a Long Short-Term Memory (LSTM) deep learning algorithm to analyze weather data from the specified region. The artificial intelligence model predicts future weather conditions with meteorological data such as temperature, humidity, wind speed and precipitation in the region. The prediction results are integrated into an algorithm structure that compares real-time data obtained from sensors in the designated agricultural field. Specifically, irrigation schedules are generated by combining the predictions with soil moisture levels. If the soil moisture level falls below predetermined thresholds, the system initiates the irrigation process. Furthermore, the system avoids unnecessary watering by aligning with weather forecasts. This approach aims to optimize water consumption while adequately meeting the plants' water requirements, thus ensuring sustainable water management in agriculture. The system's control, including the processing of analog data from the sensors and the comparison of LSTM forecast results, is managed by a single-board computer. The studies conducted were supported within the scope of TUBITAK 2209-A University Students Research Projects Support Program. In conclusion, the artificial intelligence-supported irrigation system developed to reduce water consumption and increase productivity in agriculture has been observed to yield successful results with experimental applications in the Thrace Region. The scalable structure of the system shows that it can be used effectively in different regions and under various climatic conditions. Moreover, this system offers a new perspective on agricultural irrigation processes and is expected to play a significant role in future agricultural production processes.