Sustainable agriculture depends on effective water management, especially in arid areas like Egypt's Nile Delta. This paper describes a complete smart irrigation system that combines a high-performance hybrid machine learning model for predictive soil moisture analysis with an inexpensive Internet of Things (IoT) sensor network. The hardware utilizes a sensor node topology communicating via Bluetooth Low Energy (BLE). Our proposed solution is a hybrid system based on the Random Forest (RF) and the Long Short-Term Memory (LSTM) networks. The hybrid system, after being trained on 20 years of agroweather data, achieves a 16% improvement over the MAE of the LSTM approach. The hybrid system achieves a 23% reduction in water consumption.