Agriculture is crucial for food production globally, especially with the growing population. Efficient management is essential to meet the increasing demand for food. In some regions, agriculture relies solely on unpredictable monsoon rainfall for water. To meet crop water needs, irrigation delivers water based on soil type and crop requirements, contributing to water conservation. The smart irrigation system utilizes edge and fog computing to provide a cutting-edge solution for efficient water management in agriculture. Sensors collect data on soil moisture, temperature, and humidity, and process it locally at the edge for immediate responses to changing conditions. The data is then analyzed by fog nodes to provide comprehensive insights and facilitate predictive analytics. This approach not only optimizes water usage, ensuring crops receive precise irrigation based on real-time needs but also enhances scalability and reliability. The system demonstrates significant potential in promoting sustainable agricultural practices, reducing water wastage, and increasing crop yields. This paper uses an Arduino Uno R3 microcontroller board based on the ATmega328P to implement the system. This study also utilizes Deep Learning (DL) to predict the irrigation system. The successful implementation and results of the Long Short Term Memory (LSTM) based DL model highlight a promising direction for future research in smart irrigation and environmental sustainability.