This study aims to develop an intelligent decision support system (DSS) for predicting quail egg production using the Long Short-Term Memory (LSTM) algorithm. Quail egg production is influenced by various environmental factors such as temperature and humidity, as well as farm management. With the increasing market demand for quail eggs and the importance of accurate production predictions, a machine learning-based approach is needed to support farmers' decision-making. The research data was obtained from the Bogor Regency Fisheries and Livestock Service, which includes attributes of temperature, humidity, and daily egg production. The research method uses a Knowledge Discovery in Database (KDD) approach, which consists of data selection, preprocessing, transformation, data mining using LSTM, and model evaluation using RMSE and MAPE metrics. The evaluation results show that the LSTM model provides excellent prediction performance, especially with a training data split of 70% and 60%. The system was implemented using a combination of Python (for the model) and Laravel (for the web interface). This research is expected to assist farmers in optimizing quail egg production management in a more efficient and adaptive manner based on data.