This study presents a hybrid Long Short-Term Memory (LSTM) approach to predict irrigation scheduling in maize fields in Bursa, Turkey. The LSTM model, combined with the Aquacrop 7.0 model, was used to simulate soil moisture content (MC) data due to data limitations in the investigated fields. The study aimed to enhance the accuracy of irrigation scheduling by predicting soil MC reductions based on soil, weather, and satellite-based plant vegetation data. The LSTM model was trained and tuned using a combination of these data sources, and its performance was evaluated using standard metrics such as R² and RMSE. The results showed that the LSTM model, supported by Aquacrop 7.0 simulations, effectively predicted MC reductions across various time phases of the maize growing season, with R² values ranging from 0.8163 to 0.9181 for Field 1 and from 0.7602 to 0.8417 for Field 2. This indicates the potential of the hybrid LSTM approach for precise and efficient agricultural irrigation practices. The study highlights the importance of integrating advanced computational technologies, such as machine learning, with traditional deterministic models to overcome limitations in data availability and improve the accuracy of irrigation scheduling.This study presents a hybrid Long Short-Term Memory (LSTM) approach to predict irrigation scheduling in maize fields in Bursa, Turkey. The LSTM model, combined with the Aquacrop 7.0 model, was used to simulate soil moisture content (MC) data due to data limitations in the investigated fields. The study aimed to enhance the accuracy of irrigation scheduling by predicting soil MC reductions based on soil, weather, and satellite-based plant vegetation data. The LSTM model was trained and tuned using a combination of these data sources, and its performance was evaluated using standard metrics such as R² and RMSE. The results showed that the LSTM model, supported by Aquacrop 7.0 simulations, effectively predicted MC reductions across various time phases of the maize growing season, with R² values ranging from 0.8163 to 0.9181 for Field 1 and from 0.7602 to 0.8417 for Field 2. This indicates the potential of the hybrid LSTM approach for precise and efficient agricultural irrigation practices. The study highlights the importance of integrating advanced computational technologies, such as machine learning, with traditional deterministic models to overcome limitations in data availability and improve the accuracy of irrigation scheduling.