Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/4131 2018 2018 Collage of nursing, Baghdad University, Baghdad, Iraq Maysaa Maysaa Department of Computer Science, Dijlah University Collage, Baghdad, Iraq Saif M. Ali Center for Data Science and Sustainable Technologies, INTI International University, Malaysia Deshinta Arrova Dewi Recently, irrigation management has been considered one of the most significant areas of research in smart vertical farming. Hence, it is essential to optimize freshwater usage for smart vertical farming management due to the lack of freshwater sources. It is observed that the soil moisture level and temperature data need to be appropriately examined and analyzed to predict the water irrigation level in a smart farming platform. Hence, in this work, the Internet of Things (IoT) sensors have been utilized to collect and monitor the soil moisture level, ambient temperature level, and humidity level data effectively. Besides, the collected sensor information has been analyzed and predicted to recognize the appropriate utilization of the optimum level of freshwater using Grey Wolf optimizer integrated recurrent network models. Therefore, this approach successfully analyzes the sensors' data and predicts the required level of irrigation based on motor ON and OFF conditions. The generated data from the sensor has been evaluated using the Keras model using the python language, and the performance is assessed based on the accuracy ratio. This model obtained a maximum of (0.995%) accuracy in forecasting the optimum irrigation level. The proposed system will utilize less voltage to minimize the power consumption ratio up to 35% in the irrigation process with 99.5% accuracy in forecasting the optimum irrigation level. 2026 2026 369 382 10.54216/FPA.210223 https://www.americaspg.com/articleinfo/3/show/4131